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P A R T I Research Overview
9 1.1 Background Monitoring the performance of snow and ice control operations has increasingly become an important task for highway agencies and contractors because of increased and changing stakeholder expectations. Different indicators have been used to assess performance in the United States and abroad, with varying degrees of success. Furthermore, performance comparison across and within agencies is limited since no single measure applicable to the different roadway classifications, storm characteristics, or traffic conditions is widely accepted. Key components in implementing performance measures are the identification of means for collecting and quan- tifying relevant information and the methods for establishing level-of-service (LOS) targets. Research was needed to develop a guide for applying performance measures to snow and ice control operations that addresses these and other relevant issues. Such a guide would help highway agencies and contractors monitor their level of performance and make appropriate adjustments to manage resources effectively for snow and ice control operations. 1.2 Research Objective The objective of this research was to develop a guide for applying performance measures to snow and ice control operations that would be appropriate for assessing agency and contractor performance, with a focus on safety, mobility, and sustainability. 1.3 Research Context Responding to snow and ice weather events in a way that minimizes the negative safety, economic, and social impacts is a consistent and critical duty of many transportation agencies in the United States. Snow and ice control is a complex activity that uses equipment, materials, business processes, weather information, technology, tactics, and personnel to remove snow and ice from roads, maintain traction, and restore LOS as quickly as possible. Boselly (2008) provides an update to the AASHTO snow and ice guide that illustrates how developments in equipment, materials, weather, and road weather information have informed state and local agency practices on snow and ice control. Many factors drive the use of measurement to understand and improve performance with respect to snow and ice control. The cost, both in terms of safety and economic impacts, of snow and ice removal are large. Over 3,000 traffic fatalities in 2011 were attributed to inclement weather (NHTSA 2015), and more than 70% of the population lives in areas affected by snow and ice (FHWA 2015a). Winter maintenance operations (of which snow and ice removal is a major component) are a significant portion of state department of transportation (DOT) budgets. C H A P T E R 1 Introduction
10 Performance Measures in Snow and Ice Control Operations Fay et al. (2013) note that winter maintenance makes up roughly 20% of state DOT budgets. State and local statistics on expenditures for snow and ice removal are available on an annual basis as part of the Highway Statistics publication series, a data compilation created and main- tained by the FHWA Office of Highway Policy Information. Figure 1 shows nationwide state and local government expenditures for snow and ice removal over the period of 2001 through 2013 (FHWA 2015b). Figure 1 also illustrates the variability in costs on a year-to-year basis. NCHRP Synthesis 344: Winter Highway Operations notes that âmaintenance staff contend with increasing expectations, tighter budgetary and environmental constraints, and greater demands on information ingestion, collection, and generationâ (Conger 2005). The cost of winter maintenance continues to be at the forefront of agency interest. A study by the New York City Comptroller quantified the cost per inch of snow removal in New York City, noting that âthe old adage of $1 million per inch of snow removal is also rarely true. While costs can dip that low and even lower in certain optimal years, from FY 2003 through FY 2014, the average cost-per-inch was $1.8 millionâ (Stringer 2015). While costs are driving decisions and are subject to scrutiny, a closely related (and probably more important) question is, âHow does the expenditure on snow and ice control translate to system performance?â Transportation agencies have been collecting data and measuring perfor- mance on snow and ice removal for many years. However, methods and performance measures vary significantly across agencies and regions, as do comparison techniques. Conger noted that information on the use of performance measures is limited; a survey conducted as part of the NCHRP Synthesis 344 showed that more than half of the responding agencies stated that they did not use performance measures (Conger 2005). However, this is changing rapidly. A survey con- ducted in 2017 highlighted that most of the 51 agencies surveyed stated that using performance metrics had become a routine part of their duties (Xu et al. 2017). The complexity of snow and ice control arises from the climatic differences across locations, as well as from the many differences between agencies and regions in how snow and ice response is handled. This was noted by Welch and McConkie, who stated, âNowhere else in highway main- tenance activities are the uniqueness and differences of procedure and equipment so notable as Source: FHWA 2015b. Figure 1. Nationwide state and local government expenditures for snow and ice removal on a yearly basis.
Introduction 11 in snow and ice controlâ (Welch and McConkie 1976). Differences in operational objectives, equipment, material types, procedures, and workforces are found not only between agencies but also within different districts/regions under the same agency, and even public interest and awareness of the issue can affect how a public agency views its role in snow and ice control. Maze et al., as part of the NCHRP Project 6-17, âPerformance Measures for Snow and Ice Control Operations,â provided a comprehensive assessment of trends in performance measure- ment and identified indicators and measures that were in use at the time to measure snow and ice control performance, then categorized, defined, and assessed them for usefulness (Maze et al. 2007). The research conducted for this study included a survey that noted that 33 agencies, including 16 state and provincial agencies and 17 local agencies, reported that they measured performance in snow and ice control operations. Measures reported by the survey included: â¢ Time to bare pavement, â¢ Time to wet pavement, â¢ Time to return to near normal, â¢ Time to provide one wheel track, â¢ Friction, â¢ LOS, â¢ Travel speed, â¢ Customer satisfaction, â¢ Crashes per vehicle miles, â¢ Traffic volumes during storms, â¢ Time to traffic-normal, â¢ Fuel usage, â¢ Lane miles (ln-mi, or ln-km) plowed, â¢ Personnel hours, â¢ Overtime hours, â¢ Tons of materials used, â¢ Amount of equipment deployment, â¢ Miles (km) traveled â plow down, â¢ Cost of operations (ln-mi), and â¢ Percentage of salt spreaders calibrated. The study did not provide information on the diversity of these measures. However, the measures span inputs (what resources were used), outputs (what work was accomplished), and outcomes (the results). The study, recognizing the variability of the types of agencies, snow and ice removal practices, and objectives, did not recommend a core set of measures. Rather, the study established a robust framework for developing a performance measure toolbox that is tailored to the requirements of a region. The steps identified in the study were: 1. Confirm snow and ice control operations role, 2. Identify the key snow and ice control activities and outputs, 3. Identify program stakeholders and issues, 4. Identify what snow and ice control operations need to accomplish, 5. Identify responses and performance requirements, 6. Identify potential performance measures, 7. Establish information capabilities and a baseline for each measure, 8. Assess adequacy of performance measures, and 9. Establish accountability and resources for implementation. Following the release of the NCHRP Project 6-17 publications (Maze et al. 2007), the need for standardized performance measures for snow and ice control (across states) was identified
12 Performance Measures in Snow and Ice Control Operations as a top priority (Scott 2007), and âestablishing consistent national LOS and performance mea- sures for winter maintenanceâ was identified as a priority for research by Nixon (2010). Since the NCHRP Project 6-17 publications, agencies have continued to make progress in developing performance measurement frameworks for snow and ice control (or winter maintenance in general). A study from FHWA surveyed 40 state DOTs and found that 23 reported regularly collecting and reporting road weather performance measures through dashboards, winter main- tenance reports, seasonal summaries, and so forth. (FHWA 2015a). Pletan et al., in a study on best practices for winter maintenance, noted that interjurisdictional relationships are important for promoting consistent LOS between otherwise arbitrary govern- mental boundaries (Pletan et al. 2009). The study also noted that more work is needed to develop improved outcome-based and customer-oriented performance measures (e.g., regain time, friction measurement, speed monitoring, and road closure frequency/duration). Consistent and robust measures for snow and ice control have the potential to increase the focus on improving performance in this area. The present higher frequency of extreme weather events across the country means that the magnitude of benefits that can be achieved through improvements in snow and ice control operations is higher than ever. [Overall, there were twice as many extreme winter storms in the United States in the second half of the 20th century as there were in the first half (National Centers for Environmental Information 2017).] Further, most transportation agencies, for regulatory, environmental, and accountability reasons, increas- ingly need performance measures to communicate with the public and decision makers about the effectiveness of their operations. 1.4 Organization of the Report Part I of this report is organized in five chapters. In addition to this introductory chapter, Chapter 2 summarizes the research approach, Chapter 3 details the findings of the research, Chapter 4 describes the performance measures and the approaches to quantify them, and Chapter 5 presents the conclusions. References are provided at the end of Part I. In Part II, a reader-friendly version of the critical findings of the research has been compiled for public agencies as the Guide for Performance Measures in Snow and Ice Control Operations. This guide provides a step-by-step approach to determining measures of interest and developing a performance management program around snow and ice control. In addition, an Excel-based tool has been developed to help agencies identify what performance measures meet their needs and objectives. A user guide has also been provided to help navigate the Excel tool. The tool and user guide can be found on the TRB website (www.TRB.org) by searching for âNCHRP Research Report 889.â
13 The study methodology included the following steps: â¢ Step 1: Conduct literature review, â¢ Step 2: Evaluate performance measures, â¢ Step 3: Identify core performance measures, â¢ Step 4: Develop guide, and â¢ Step 5: Develop support tools and materials. 2.1 Literature Review The research team conducted a literature review and documented current practices reported in the United States and internationally for snow and ice control performance measures, focusing on the following questions: â¢ What are the new performance measures beyond those identified in NCHRP Project 6-17, especially for the core categories of bare pavement, traffic speeds, and crash risk? â¢ How does the use of measures vary by agency type in the United States (especially if private contracting is used)? â¢ What is the current level of usage of winter severity indices and other normalization processes? â¢ What are the differences between U.S. and international practices for snow and ice control performance measurement? â¢ How are agencies using snow and ice performance measures in decision making? â¢ How are agencies incorporating sustainability considerations into winter maintenance operations, and how does this affect performance? As part of this review, information on standard practices from the United Kingdom, Europe, Asia, and Canada was compiled. The team compared and contrasted observed international practices with U.S. practices, paying special attention to the approaches used for measuring friction, approaches for normalization for seasonality and regions, and use of weather and climate data. Finally, literature was reviewed to identify trends and crosscutting issues that influence the quantification of performance measures. The decade prior to the time of this research saw a great deal of change in state DOT practices with respect to both agency capability and interest in performance measurement. The research on trends and themes focused on the following areas: â¢ Growth in technology, especially for road condition reporting systems and mobile data technology. This growth has enabled new applications and process improvements in winter maintenance. For example, data flows from mobile data terminals in snowplows are changing winter maintenance tactics significantly, allowing for data-driven decision making and route and fleet optimization. C H A P T E R 2 Research Approach
14 Performance Measures in Snow and Ice Control Operations â¢ Increased sophistication and improvements in winter maintenance responses at agencies. These include better training of staff in effective winter maintenance and proactive approaches to management, which are improving agenciesâ abilities to manage adverse weather and achieve better outcomes. â¢ Increased engagement with the traveling public. With the advent of social media, travelers are now communicating more actively with state and local agencies about snow and ice control. For example, the Wyoming and Utah DOT citizen reporting programs offer new ways to measure snow and ice control performance based on the travelerâs experience. â¢ Growth in the availability of traffic data (both fixed and probe-based data), weather data, and road condition data. The recent effort in South Dakota to link the Maintenance Decision Support System (MDSS) to the road condition reporting system (RCRS) illustrates how data are supporting maintenance and traveler information needs during snow and ice control opera- tions. The availability of such data sets offers new ways for agencies to monitor performance at strategic and tactical levels. â¢ The increased importance of measuring reliability by state and local agencies. Snow and ice present a significant hurdle to travel reliability, and measures like the planning time index (PTI) and buffer index (BI) add useful indicators to the snow and ice control performance measurement toolbox. â¢ Recognition of sustainability of winter maintenance as a priority at many agencies. Winter maintenance activities affect social (mobility, access, equity), economic (cost of maintenance, loss of economic activity), and environmental (use of deicing materials) com- ponents of sustainability. 2.2 Evaluation of Performance Measures From the literature, a list was compiled of currently used performance measures and indica- tors, and a taxonomy was developed to collect information on those needed for evaluation and consideration as potential core measures. More than 50 indicators and measures were identified and, based on an assessment of their characteristics and impacts, they were classified into the following 10 categories: 1. Storm characteristics/severity; 2. Material management; 3. Labor resource allocations; 4. Level of maintenance response; 5. Maintenance response outcomes; 6. Level of operational responses; 7. Traveler experience, mobility, and safety; 8. Cost, budget, and funding; 9. Transportation resilience; and 10. Economic activity. Relevant information on each indicator and measure was compiled and is provided in the tab âTaxonomy of Performance Elements for Snow and Ice Controlâ within the Excel tool. The taxonomy fields are shown in Table 1. A consistent set of criteria was established to support both the identification and the evaluation of a set of core performance measures. Adams et al. described principles to guide the selection of performance measures that also inform the evaluation of measures and noted that performance measures should: â¢ Be meaningful to and appropriate for the needs and concerns of decision makers; â¢ Reflect specific goals or guidelines;
Research Approach 15 â¢ Reflect current issues; â¢ Allow for comparison of products, equipment, and so forth; â¢ Allow for prediction of future trends in planning and budgets; and â¢ Allow for comparison of performance across states (Adams et al. 2003). Building from this list, expanded criteria for assessment of performance measures were developed. Table 2 lists the criteria used to evaluate the performance elements identified in this study. Establishment of criteria provides a means for evaluating measures to consider their suitability to support decision making and identify their strengths and limitations. For example, the measure could be scored on the evaluation criteria using a high, medium, or low scale across all the criteria. Similarly, criteria weights could be developed based on expert judgment or agency needs. Individual agencies can customize the weights based on their priorities or any unique circumstances. The compilation of performance measures revealed a wide variety of practices and measures used by state DOTs and transportation agencies in other countries. A few common definitions and accepted standards for performance measurement could be suggested for adoption. Both U.S. and international practices rely on data collected both manually (field personnel) and auto- matically from winter maintenance systems (fleets and sensors). Key findings from the analysis of documented measures are as follows: â¢ Agencies find it easier to measure inputs and outputs than outcomes. However, inputs and outputs are highly sensitive to agency policies and constraints. For example, the prioritiza- tion of routes, whether the agency uses anti-icing or not, and resource procurement practices (which affect costs) all play a major role in determining the magnitude of these values. â¢ Even outcome measures used by agencies are not adequate reflectors of performance due to factors like storm severity and the inability to identify the impact of the winter maintenance activities in achieving outcomes. In other words, the differences in outcomes with or without winter maintenance activities are hard to establish. Taxonomy Field Detail Definition of element Definition of the element Type Input/output/outcome measure Unit Typical unit of measurement Data required Typical data required to calculate the performance measure Data collection method Required data collection approach (manual or automated) Geographic scale Physical scale of measurement (segment or district level, etc.) Event scale Type of winter event applicable Temporal scale Whether the measure can be used in real-time or in post-event evaluation Types of maintenance activity affected Relevant snow and ice activities Systems/technology required Systems or technologies required to collect or analyze data (MDSS, AVL, ITS infrastructure, private sources of data, etc.) Type of decision support Types of snow and ice decisions supported (materials use, labor allocation, etc.) Variants Similar measures that address the same performance criteria Other Contextual considerations (urban/rural, type of roadway, etc.) Note: AVL = automatic vehicle location; ITS = Intelligent Transportation System. Table 1. Categorization taxonomy for snow and ice performance elements.
16 Performance Measures in Snow and Ice Control Operations Criteria Rating Guide High Medium Low Measurable Measurement is straightforward and objective. Measurement is relatively straightforward but subject to some interpretation or assumptions. Difficult to measure; requires observation, estimation, or formation of assumptions to measure. Controllable Transportation agency actions have a direct impact on performance with respect to the measure. Transportation agency actions can affect performance on this measure, along with other factors. Transportation agency actions account for only a portion of performance on this measure. Easy to monitor Data are collected regularly in an automated fashion, and little work is required to monitor. Data are collected regularly, but frequent monitoring requires some agency resources. Data are collected in a more laborious fashion or data collection requires employment of at least one staff person to assemble and analyze data for this measure. Robust Findings are reliably accurate, timely, and resistant to errors even when deviations from assumptions occur. The measure is generally accurate and reliable, with some exceptions (slightly more susceptible to error). The relationship between findings and performance is strong but imperfect; it may be more difficult to measure performance accurately for unusual events. The measure and methods/models used to calculate performance are prone to significant errors or inaccuracies, or they operate under relatively narrow assumptions. Supports benchmarking and decision making The correlation between investments made and outcomes the measure identifies is clear (after controlling for factors such as winter and storm severity), maximizing its usefulness for decision makers (e.g., to understand the impacts of investments). Measurements are relatively consistent across jurisdiction, enabling reliable benchmarking. Performance on the measure is improved when investments and attention to performance are made; however, the relationship is not direct or the measure is not easily understood. The ability of decision making to influence performance is positive, but the relationship may be indirect. The measureâs usefulness for benchmarking and decision makingâwhether due to accuracy issues, lack of correlation between investments or performance, or lack of benchmarking capabilitiesâis limited. Acceptance in maintenance community The measure is widely used and widely accepted as a top choice for measurement of performance for a particular topic. The measure could even have widely accepted standards for measurement. The measure is used by some agencies, and there is a general understanding of the measure and its value. The measure is relatively obscure and is only used by a few agencies. Intuitive The measure makes sense to a lay audience with no background in transportation operations and maintenance. The measure can be understood by most members of a lay audience after an explanation. A brief explanation of the measure may fail to bring clarity to a significant portion of a lay audience. Universality This measure can be applied with minimal or no modifications by a wide variety of agencies and locations. This measure applies to a large number of agencies but might not apply to some agencies or locations based on practices and conditions. This measure is limited to specific practices and agencies and is not widely used outside those agencies. Table 2. Evaluation criteria developed for this project.
Research Approach 17 â¢ Resource usage measures that document material, fuel, and labor use are formed frequently in agency performance reports. In best cases, they are reported along with an indication of the severity of the season (which may be a calculated index or a measure of weather incidence such as weather hours). â¢ Outcome measures have primarily focused on segment recovery indicators, such as frequency to achieve defined targets for bare pavement, speed recovery, and grip factor. â¢ Further downstream, outcome measures such as road userâs savings and reduction in lost economic activity are still primarily in the research or analytical stages and are not reported by winter maintenance agencies as part of their performance measurement frameworks. â¢ New interests and objectivesâranging from transportation resilience during extreme weather, environmental stewardship, risk-based asset management, and real-time reporting regulationsâcreate new requirements for performance measures. However, most are still in the research stage and have not been widely adopted by maintenance agencies. 2.3 Identification of Core Performance Measures For this guidance, a core set of measures were identified for safety, mobility, and sustainability, as shown in Figure 2 and further described in Chapter 4. In identifying these measures, the research team looked at various input-output-outcome-impact categories and measures that fell under each category. Input and output measures are important to agencies in informing day-to-day tactics and decision making about event response. However, for the guidance provided in this report, the focus is on the outcome and impact end of the spectrum and is geared toward enabling a greater consistency in collecting, analyzing, and reporting outcomes and impacts associated with snow and ice control operations. To that end, measures provided in this guidance can be generally considered lag measures that are primarily more suited for retrospective and strategic decision making. These measures include mobility, safety, and sustainability measures that are closely linked with each other. These measures have widespread applicability across different agency types, contracting mechanisms, and regions. Safety and mobility measures also closely correspond to the direction provided by FHWAâs performance management final rules (FHWA 2017b) and Strategic Highway Research Program 2 (SHRP 2) research on travel reliability (FHWA 2017c). It is important to note that sustainability in this context is defined from an agencyâs perspec- tive rather than a societal perspective. From an agencyâs perspective, sustainable operations are defined by their environmental stewardship, efficiency of response, and the satisfaction of their traveling public. Figure 3 shows the applicability of performance measures in terms of the timeline of a storm (before, during, and after a storm event). Safety measures are typically collected during and after Safety Injuries Mobility LOS Recovery Reliability Sustainability Efficiency Customer Satisfaction Environmental Fatalities Figure 2. Core set of performance measures.
18 Performance Measures in Snow and Ice Control Operations the event. Mobility and customer satisfaction measures are primarily collected during the same time frame. Efficiency and environmental stewardship measures are collected throughout the winter response season. However, this does not suggest that data should only be collected during the highlighted time; this should be done continuously. 2.4 Development of a Guide A guide for evaluating selected performance measures was developed as part of this project (see Part II). The guide builds on efforts that can be summarized into two general tasks: â¢ A review of available literature regarding current national and international practices in snow and ice control operations, including documentation of emerging trends and crosscutting issues; and â¢ The identification, categorization, and evaluation of potential performance measures related to snow and ice control operations. The following subsections provide an overview of the guide. 2.4.1 Guide Objective and Purpose The objective of the guide is to present a core set of performance measures related to snow and ice control operations. The guide is appropriate for assessing agency and contractor performance with a focus on safety, mobility, and sustainability. Moving beyond just performance measures, the purpose of the guide is to provide insight on developing a performance framework for snow and ice control operation management, helping decision makers to identify appropriate adjustments that can be made to manage resources effectively through the use of the proposed performance measures. The guide highlights some of the best practices in winter maintenance and provides detailed procedures that highway agencies and contractors can follow to monitor their level of performance with a focus on safety, mobility, and sustainability. The guide and supporting materials provide the approach for identifying the performance measures, the means for their quantification, the methods for setting targets, and other relevant information. BEFORE DURING AFTER STORM TIMELINE LOS RECOVERY EFFICIENCY FATALITIES INJURIES RELIABILITY CUSTOMER SATISFACTION ENVIRONMENTAL STEWARDSHIP MOBILITY MEASURES SAFETY MEASURES SUSTAINABILITY MEASURES Figure 3. Applicability of performance measures with respect to the timeline of a storm.
Research Approach 19 While the guide mainly focuses on roadways, it is a flexible and inclusive document. Many of the concepts described in the guide are capable of being adapted to measure the performance of winter maintenance operations that focus on other infrastructure (such as bike paths and sidewalks) and services (such as transit). 2.4.2 Guide Audience The target audience for the guidebook is staff, particularly those in management roles, at agencies (such as those of state and local governments) responsible for snow and ice control. The guidebook is intended to be a resource in considering how these agencies can monitor their level of performance with respect to a number of different aspects of winter maintenance and consider making appropriate adjustments to manage resources more effectively. The content of this guide will also be of interest to contractors that work closely with highway agencies and seek to understand how to evaluate their performance and communicate with their clients about it. Policy makers seeking to understand how performance with respect to winter maintenance activities can be measured while taking into account the many external factors that influence performance would also benefit from the contents of the guide, which contains examples from peer agencies. Finally, researchers and contractors for agencies responsible for winter mainte- nance can use the guide as a resource for information about the state of the practice with respect to performance measurement for winter maintenance. 2.4.3 Guide Overview The guide contains an introductory chapter followed by four chapters that describe the 10 key steps to develop and assess performance measures (as shown in Figure 4). The steps provide a Chapter IV: Reinforcing Performance-Based Management Step 9 â Integrate into decision making Step 10 â Evaluate process and identify improvements Chapter III: Using Performance Information Step 7 â Set targets and establish baseline Step 8 â Report performance Chapter II: Implementing Performance Measures Step 5 â Inventory current practices and gaps Step 6 â Identify data sources and needs Chapter I: Defining Performance Measures Step 1 â Review mission and goals Step 2 â Refine operational objectives Step 3 â Identify performance measures Step 4 â Develop analytic approaches Figure 4. Key chapters and steps within the guide.
20 Performance Measures in Snow and Ice Control Operations systematic process that allows agencies to define, implement, use, and evaluate performance measures that satisfy their needs. The content and structure of the guide were created with the following principles in mind: 1. Use a rational approach that allows flexibility for the agencies and users of the guide. 2. The guidance needs to be succinct and directly actionable since the audience is likely to have limited time. 3. The guidance needs to support agencies with different capabilities, from those that are beginning the process of collecting limited input and output measures to agencies that are already able to adequately capture outcome measures. 2.5 Supporting Tools and Development of Materials Supporting materials, including an Excel tool and a tool user guide, allow for the implementa- tion of the guidance developed in this project. The interactive Excel-based tool and the associated user guide provide a decision guide for agencies to customize their performance measurement practices to meet their needs and objectives.
21 3.1 Current Practices for Snow and Ice Control Performance Measurement Adams et al. described principles to guide the selection of performance measures. The measures should: â¢ Be meaningful and appropriate to the needs and concerns of decision makers; â¢ Reflect specific goals or guidelines; â¢ Reflect current issues; â¢ Allow for comparison of products, equipment, and so forth; â¢ Allow for prediction of future trends in planning and budgets; and â¢ Allow for comparison of performance across states (Adams et al. 2003). Similarly, Karlaftis and Kepaptsoglou listed the following characteristics of effective perfor- mance metrics as important: â¢ Relevance: metrics must be applicable to the planning and budgeting needs of agencies, â¢ Clarity: metrics must be clearly defined to avoid misinterpretation, â¢ Reliability: bias or errors should be avoided through the implementation of standardized measurement processes, â¢ Precision: data collected should be as precise as possible, and â¢ Availability: data should be collectable in a cost-effective manner, and outcomes should be readily accessible by management and other stakeholders (Karlaftis and Kepaptsoglou 2012). A 2017 report found that 51 surveyed agencies ranked (on a scale of 1 to 5) their performance goals as follows: safety, 4.98; mobility, 4.5; economy, 4.1; essential functions, 4.0; environment, 3.5; infrastructure, 3.3; and livability, 2.9 (Xu et al. 2017). As such, it is expected that the most commonly used performance measures for winter operations are various measures of road surface condition (e.g., time to bare pavement; time to bare wheel path; time to clear condition of the road, or roadway cleared shoulder to shoulder; and friction)âmost likely because this information is easy to collect and use (Maze et al. 2007; CTC and Associates and Wisconsin DOT 2009). Other identified performance measures are time to return to a reasonable near-normal condition, length of road closures, crash reduction, and customer satisfaction (Maze et al. 2007). Some approaches use visual inspection, such as the pavement snow and ice condition (PSIC) index, which characterizes roadway conditions (Blackburn et al. 2004) and can be used to assess during-storm and post-storm performance. PSIC is easy to use and low cost, but is deficient in that it is subjective because it is based on the perspective of the individual collecting the information. Friction measurements have been identified as an indicator of road condition that can be used to measure performance (Maze et al. 2007; Fay et al. 2013). C H A P T E R 3 Findings and Applications
22 Performance Measures in Snow and Ice Control Operations Other research activities in snow and ice performance are discussed in the following: â¢ In a pilot study in Michigan, Bandara (2015) developed a relationship between visually observed pavement conditions and measured friction. Three storm events were considered as part of the study, which provides some indication of how objective measurements of friction are related to more subjective visual measurements of pavement condition. â¢ Cao et al. (2013) described a study focusing on the impact of winter weather and road surface conditions on the average vehicle speed on rural highways to investigate the feasibility of using traffic speed from traffic sensors as an indicator of the performance of winter road main tenance. The modeling showed a statistically strong relationship between traffic speed and road surface conditions, suggesting that speed could potentially be used as an indicator of bare pavement conditions and thus the performance of winter road maintenance operations. â¢ Qiu and Nixon (2009) developed a model to quantify the average free-flow traffic drop caused by winter storm events. As part of this effort, a storm severity index (SSI) was developed, which ranked each winter storm on a scale between 0 (a very benign storm) and 1 (the worst imaginable storm). The result of this study was a performance measure based on average vehicle speed. A maximum expected average speed reduction was identified for each class of road. For a given storm, this maximum expected average speed reduction was modified by the SSI to give a target average speed reduction. If the measured speed reduction was less than the target speed reduction, the winter maintenance activity was assumed to have achieved its intended effect. â¢ Greenfield et al. (2012) developed a model for real-time winter road performance analysis that estimates expected traffic speed reductions at a given time during a winter storm using commonly reported and forecast road weather data. â¢ Kwon (2012) developed an automatic process to identify the road condition recovery times during snow from traffic-flow data from existing Intelligent Transportation System (ITS) infrastructure. â¢ Fu et al. (2013) developed a performance measurement framework that linked criteria for selection of performance measures with a set of commonly used performance measures. â¢ Levola and Pakkala (2014) reported on a Finnish approach that linked customer satisfaction and safety targets in road performanceâbased maintenance contracts. â¢ Mirtorabi and Fu (2013) introduced an index that included three severity indicators defined at disaggregate spatial and temporal levels, including a resource index, a safety impact index, and a mobility impact index. â¢ Usman et al. (2010) presented a study showing the empirical relationship between safety and road surface conditions at a disaggregate level (event based), making it feasible to quantify the safety benefits of maintenance. The authors used a road condition index as a measure to capture surface conditions and found it related to accident occurrence but not to the specific maintenance activity performed. â¢ Veneziano et al. (2010, 2013) developed a toolkit to conduct costâbenefit analyses for a series of winter maintenance practices as well as equipment and operations. Ten specific items were considered: â Comparing flexible blades to traditional blades, â Pre-wetting at the spreader, â Spreader calibration, â Slurries, â Tow plows, â Contracted truck (private or municipal) versus state-owned truck, â Open versus closed-loop spreader controls, â Remote cameras for monitoring remote locations,
Findings and Applications 23 â Laser guides, and â Tailgate versus hopper spreaders. â¢ Ye et al. (2012) developed methods to quantify the costs and benefits of winter highway maintenance. The report identified the general benefits of winter maintenance, established which ones were quantifiable, and determined whether any methodologies or approaches existed to quantify these benefits using agency data. The benefits that could be directly quantified using existing agency data included safety improvements (reduced crashes), travel time savings, and fuel savings. Consequently, methodologies were developed to estimate each of these. To estimate safety benefits, a model was established to predict the number of crashes that could be expected to occur under different winter maintenance scenarios. The changes (ideally reductions) in crashes and the financial savings as a result of improved maintenance represent the benefits of winter maintenance on safety. Using 2001 through 2006 winter season data from the Minnesota DOT, the researchers estimated the benefits of winter high- way maintenance by the Minnesota DOT to be $201 million per winter season. â¢ Nordlof (2014) presented the Swedish Winter Model, which comprehensively estimates costs and benefits of changing winter maintenance standards. Some findings can be extracted from this research: â¢ Integration of traffic operations data with maintenance data continues to be of interest. Because speed, incident, and volume data are more readily available, their role in snow and ice control performance measurement is expected to grow. â¢ Methods to establish overall costâbenefit information and the relative impact of maintenance practices continue to be investigated. No comprehensive model exists in the United States. â¢ Factoring in severity continues to be a challenge for implementing agencies. 3.2 New State DOT Practices and Approaches The increased availability of road weather and winter maintenance data enables transportation agencies to use performance measures to influence decision making, enabling a data-driven approach to deploying snow and ice activities to better reduce crashes and fatalities and minimize speed reductions during winter weather events. A survey conducted by the FHWA reported that 36 of the 40 respondents reported using environmental sensing station (ESS) data to support traffic management and maintenance decision making, and 23 reported regularly collecting and reporting road weather performance measures through dashboards, winter maintenance reports, seasonal summaries, and so forth (FHWA 2015a). McCullouch et al. summarized the performance measures used by various states as part of research on snow and ice standards conducted for Indiana DOT (McCullouch et al. 2013). Kipp and Sanborn surveyed state DOTs as part of a research project for the Vermont Agency of Transportation (Kipp and Sanborn 2013). State DOTs use performance measures to communicate externally, including with the traveling public, about the effectiveness and efficiency of snow and ice activities. Reporting mechanisms have been through online scorecards or dashboards, DOT performance reports, fact sheets, and other methods. Online scorecards or dashboards illustrate performance measurement results using graphics, grading or rating systems, and so forth. This format presents information about DOT activities, or winter maintenance only, in any easily digestible format. Some DOTs post annual performance measurement reports online, and these reports may include a section on winter maintenance activities. In addition, some DOTs produce fact sheets and other publications that provide information about winter storm maintenance activities. In general, few state DOTs have a dedicated, publicly available winter maintenance performance report. Some exceptions are Minnesota (Minnesota DOT 2016), Wisconsin (Wisconsin DOT 2014), and Indiana (Ivy 2013).
24 Performance Measures in Snow and Ice Control Operations Snow and ice performance measures can also help inform a number of planning, manage- ment, and operations decisions related to winter maintenance, including those related to: â¢ Material management. Understanding how operations activities (e.g., plowing and applying chemicals) affect the speed of winter weather response can help decision makers better deploy maintenance operations strategies to achieve mobility goals (Qiu 2008). â¢ Resource (budget and labor) allocations. Performance measures can be used to calculate metrics (e.g., benefitâcost ratios) to help determine and justify resource allocations (Koberlein et al. 2014). â¢ Response strategies. Comparison of the effectiveness and efficiency of winter maintenance activities on various roads and highways can help decision makers prioritize routes for winter maintenance when they are faced with resource constraints (Qiu 2008). â¢ Contractor performance. Bourdon (2001) highlights the challenges in defining appropriate language to measure performance by private contractors. The specific performance measures used by state DOTs are largely determined by the timing of the decisions. When used to influence decision making during a storm, a DOT may use mea- sures of on-the-ground conditions, which may include storm speed, solid material application rate, pavement temperature, and air temperature. For post-storm assessments, measures such as a winter storm index, winter mobility index, amount of salt or other materials used, or lane miles plowed may be more suitable. When conducting annual reviews, performance measures that summarize the entire season of activities may be beneficial, including the number of snow events, the number of freezing rain events, total snow amount, and total number of incidents. Some developments in snow and ice performance measurement have occurred in Idaho, Ohio, Vermont, and Iowa since the release of the NCHRP Project 6-17 publications, which documented some of the leading states in performance measurement at that time (Maze et al. 2007). The Idaho Transportation Department (ITD) developed and implemented three winter performance indices, which are being considered in other states and international locations (Jensen et al. 2014). These are the winter performance index, the storm severity index, and the mobility index. These indices are based on grip factors and weather readings from Road Weather Information System (RWIS) stations in Idaho. Jensen et al. (2014) report that these three indices provide ITD the capability to track its winter maintenance performance and help with the review of its operational procedures and understanding its return on investment. Kipp and Sanborn (2013) reported that, according to the Vermont Agency of Transportation Snow and Ice Control Plan, the director of operations, the maintenance transportation adminis- trator, and the district transportation administrators review the following information annually to gauge program effectiveness: â¢ Material application rates, â¢ Vehicle speeds during and after storm events, â¢ Condition of travel lanes and shoulders during and after storm events, â¢ Storm data (precipitation, air temperature, road surface temperature, wind speed, etc.), and â¢ Plowing frequency. MacAdam (2014) reported the development of the Ohio DOTâs Snow and Ice Performance Evaluator to rate Ohio DOTâs performance through an entirely data-driven analysis. The key metric was the number (and percentage) of road segments that did not recover from the speed drop within 2 hours. Weather data from the RWIS were used to calculate this metric. Iowa DOT reported the use of winter operations dashboards that provided actual and targeted salt usage and hours updated daily and by maintenance areas (Iowa DOT 2013). In addition, Iowa DOT moved toward traffic-based performance analysis (Greenfield et al. 2012).
Findings and Applications 25 3.3 International Practices Significant knowledge can be gained by examining how transportation agencies in other snow-affected countries measure performance with respect to snow and ice control. A key component of NCHRP Project 6-17 and several other research efforts was to determine how transportation agencies in other countries have managed the challenge of removing snow and ice to minimize negative effects on the transportation system, businesses, and the traveling public. The performance measures, and the framework for managing snow and ice control, vary widely between countries. The Snow and Ice Databook (World Road Association 2014) provides a country-by-country summary of how snow and ice control is managed. A summary for several countries listed in the handbook is presented in Table 3. Differences in nomenclature and technology use, however, complicate direct comparisons of practices and measures between countries. In the United Kingdom, the Code of Practice for Highway Maintenance Management directs the management of the highway network, including âwinter service,â which includes ice prevention, snow clearance, and all forms of winter maintenance activity. The code states that, âAuthorities should formally approve and adopt policies for Winter Service which are coherent with wider objectives for transport, integration, accessibility, and network management, including strategies for public transport, walking, and cycling.â The code outlines the objectives, statutory basis, development of winter service plans, and so forth, and addresses resilience and climate change (UK Department of Transport 2013). Country Framework Measures Used Technologies Used Other Notable Points Andorra â â Onboard communications equipment (TETRA); automatic laser stations on vehicles; frost sensors. â Austria Broad approach â considers maintenance of entire transportation system â includes monitoring bike and pedestrian facilities. Traffic regulations are legal foundation for all winter maintenance activities. Sidewalks must be maintained between 6 a.m. and 10 p.m. Framework includes detailed protocols based on category of roads and type of event. Matrix of maintenance activities based on road categories and weather condition for rural and urban areas. Treatment depends on measures, including amount of snowfall, resistance. Road weather info systems; 370 ice forecasting systems on highways. â Belgium Focus on using as little salt as possible while achieving desired conditions (i.e., focus on consumption, taking weather severity into account). â RWIS as decision support tool, traffic management centers with counting devices, automatic incident detection, variable message signs, automatic control system programmed with geographic coordinates, spreaders with GPS antennae, visualization and operating system. â Table 3. Summary of performance measurement practices for selected countries. (continued on next page)
26 Performance Measures in Snow and Ice Control Operations Country Framework Measures Used Technologies Used Other Notable Points Canada Quebec â 2006 Sustainable Development Act has management framework. 59 service centers in Quebec â contract with companies and municipalities to provide services. Technical committee on winter service shares best practices. Winter severity index. Quebec â performance indicator for winter road maintenance â rate of compliance indicator for contractors (service quality criteria, communication quality, compliance with deadlines). RWIS (over 250 stations in operation â subsidized 50% by Transport Canada), anti- icing systems on bridges and defense structures. Environment Canada provides real-time data quality control, national integrated RWIS database. Automatic vehicle location (AVL) and infrared sensors on patrol vehicles to verify pavement temps and adjust application of materials. Onboard equipment on vehicles for data communication and surveillance, automatic sprinkling system. Canada Environmental Protection Act of 1999 and other actions call for reductions in road salts used to lessen contamination of crops, ecosystems, and drinking water. Code of practice/best management practices identify environmentally vulnerable areas. Czech Republic â Winter index as evaluation for winter maintenance effectiveness â compares conditions and cost. Plowing and salting indices used as well. â â Denmark Private companies provide drivers and vehicles; government provides spreading equipment. Traffic information center monitors situation and sends updates to media. Danish Road Directorate uses salt index to define severity of winter maintenance. Service objectives set for type of road and class of facility, clearance time targets set. Internal â collection of data from contractors. External â data collection Salt spreaders with GPS data collection, RWIS, center for operations where emergency services and other departments are located. Has winter management system VINTERMAN. Denmark has a number of ongoing research projects to improve methods. equipment feeds into VINTERMAN system. Road sensors measure salt consumption. Finland â Friction values are an important measure used because packed snow and ice are allowed â no standard winter severity measure. Due to rural nature of many roads, resources focused on servicing those most heavily traveled. â Contractors can get customer satisfaction bonuses. Focus on reducing use of salt to protect groundwater â use of dry salt is forbidden; salt is applied as a solution or moistened prior to spreading. France â Salt consumption, cost per kilometer, number of man hours for winter maintenance, user satisfaction surveys. 800 RWIS stations â not centralized at national level. â Germany Snow clearing cycle times defined (e.g., every 2 h on highways, 24 h/day). Priority plans identify roadway types, traffic volume, special traffic, and special accident- prone spots â plans determine routes and sequences. Winter index examines correlation between winter severity and salt consumption necessary. Amount and frequency of snowfall in snowy regions are greatest determinant of salt consumption. RWIS â about 1,000 meteorological installation stations. Yearly budgets for road maintenance, not specified for specific seasons. Table 3. (Continued).
Findings and Applications 27 Country Framework Measures Used Technologies Used Other Notable Points South Korea Road authorities must meet required standards for maintenance â road levels are responsibilities of different authorities (national down to county level). Measures â material usage, costs (labor and materials) per lane- kilometer â taking fluctuations in material costs into account. Testing electrothermal snow- melting systems. â Sweden Developed a detailed winter model for maintenance. Winter and salt indices used extensively. Developed internal (salt and abrasives consumption) and external (user satisfaction) measurements of efficiency. 800 RWIS field stations. Focus on environmental impacts. Switzerland Road classes, service levels, priority levels inform decision making. Costs per vehicle-kilometer, salt consumption, internal (increasing efficiency, reducing costs, institutionalizing processes and knowledge) and external (road closure occurrences) measures of efficiency, safety measures. â â Source: World Road Association (2014). Note: â = no information available. Italy â Snow removal index, synthetic index (similar to snow but applies to other maintenance activities like grass cutting and other services). â â Japan Broad approach â considers maintenance of entire transportation system. Vision for âbarrier-free winter mobilityâ that addresses snow and ice control for transit and pedestrian facilities too. LOS standards â criteria for service by road type according to snowfall, air temp, and traffic volume. Various facilities serve as countermeasures to avalanches and snowstorms (snow fences, avalanche control fences, snow sheds). Snow-melting facilities include road heating, snow- melting sprinklers, and snow flowing gutters. Detailed information system for traveling public. New Zealand Three winter maintenance periods defined (high, marginal, low). Conditions categorized (fine, light, moderate, severe, very severe). LOS defined for different roadway types. Contractorsâ performance measured through GPS AVL system â required to meet LOS standards â success rate must be at least 95% â contractors submit records documenting all activities. â Deicing discontinued â only grit and calcium magnesium acetate used â both solid and liquid forms. Services entirely contracted. Similar to Japan, uses snow fences and other avalanche measures. Norway Pedestrian and bicycle facilities incorporated into maintenance operations. Use of contractors for most operations â competition maintains quality â evaluated based on snow and ice thickness, friction, quality of equipment, etc. Winter index established in 2003 â uses wind, temp, precipitation, etc. to make theoretical calculations about the need for winter maintenance â ultimately model was not good enough to be used as basis for contractor compensation. LOS used for different roadway classes. Ongoing research to study improved spreading sand methods with hot water, methods for better snow and slush removal, effectiveness of salt and salt residue on roads, different salt types and methods, and methods and equipment for measuring friction. â Iceland â Winter index used to distribute funding between regions â reflects temperature, humidity, wind. Costs and benefits calculated â amount of salt used, costs, lane miles treated. â Focus on decreasing salt consumption. Table 3. (Continued).
28 Performance Measures in Snow and Ice Control Operations 3.4 Challenges and Constraints to Performance Measurement The challenges to effective performance measurement in snow and ice control operations involve defining successful practices and the effectiveness of the actions performed. Mewes et al. (2012) reported that a common problem facing agencies involved in the practice of winter maintenance is the difficulty of measuring the effectiveness and efficiency of winter maintenance operations. Temporal and spatial variability in weather, resource constraints, and expected LOS, along with a general lack of quantifiable information on the results of maintenance activities, make it exceedingly difficult to measure performance. A study by Parsons Brinckerhoff (2013) noted that, among states, there is no established link between LOS and lane-mile cost; there is no established link between cost and assumptions regarding number and intensity of storms; and most state DOTs use separate financial and maintenance management systems, which makes identifying the linkages and determining the true cost of response more difficult. Often related to this is the issue of subjectivity involved in self-reported information on road condition. Greenfield et al. (2012) noted that common outcome measures include the time it takes to return the road to normal conditions, friction measurements, and public surveys. The authors noted that time-to-normal statistics are often self-reported, and the assessment of normal can be subjective. The same holds for LOS. CTC and Associates and Wisconsin DOT (2009), in a survey of the use of LOS in winter maintenance, noted that almost two-thirds of the agencies (63%) used management staff to monitor routes to determine whether LOS guidelines had been met. However, friction measurements require costly and specialized equipment that can traverse the road with sufficient frequency and spatial distribution to obtain an adequate sample. Consumer surveys are important but often collect only generalities about statewide and winter-long programs. Therefore, none of these common outcome measures provide what is desired, which is an easily collected, spatially and temporally dense, and objective outcome measurement. Tools like the MDSS, material management systems, and the RCRS can help minimize the subjectivity of collecting and processing self-reported data. For example, Deeter et al. (2014) noted the following sources of information as automatically reported data into RCRS: pavement temperature, air temperature, wind speed, precipitation type, and precipitation rates. As the use of the MDSS grows, agencies can add capabilities that allow the system to not only use weather data to make recommendations for treatment, but also to integrate mobile observations from vehicles and road condition data reported by field personnel, as well as to generate segment- level forecasts. Fu et al. (2013) noted two challenges in establishing appropriate performance measures and service standards. One challenge is that snowstorms vary over space and time considerably, making it difficult to conduct some of the common performance measurement tasks such as performance benchmarking and trend analysis. The other challenge is that the relationships between the outcomes of maintenance operations (e.g., safety and mobility), outputs (e.g., bare pavement recovery time and pavement friction), and inputs (e.g., amount of salt used and hours of operations) are confounded by many uncontrollable variables such as storm severity, road characteristics, and traffic conditions. This makes it difficult to develop performance measures and service standards that are both outcome oriented (attributable) and controllable. Differences in topography, climate, and road conditions require that severity indices be localized. For example, Utah DOT reviewed several indices used by state DOTs and developed a Utah-specific model that factored in climate and precipitation patterns found in the state (Farr and Sturges 2012). Most indices are also seasonal, with only a few that can be calculated at a storm-by-storm level. Qiuâs research in this area (Qiu 2008) has been adopted by state DOTs to a limited degree. Also, a winter severity index (WSI) does not always correlate to the level of
Findings and Applications 29 response. For example, a light winter in a heavily urban area might necessitate a higher level of response over a season than that of a rural region with a high WSI. Bourdon (2001) highlighted the challenges in defining appropriate language to measure the performance of private contractors. Although agency capabilities have improved, the study noted the steep learning curve for both public and private stakeholders to effectively define, measure, and pay for performance. 3.5 Emerging Trends and Crosscutting Issues Snow and ice control performance measurement does not occur in a vacuum and is influ- enced by trends and crosscutting issues that occur elsewhere in the transportation field. Some of the challenges identified in the previous section may be mitigated by emerging trends in snow and ice control. The following trends and issues have been identified as having a direct bear- ing on how snow and ice control performance is defined, measured, and calculated. 3.5.1 Advancements in Technology for Snow and Ice Control and Performance Measurement New and improved tools for tracking inputs such as vehicle miles traveled (VMT) and materials used, real-time decision support systems that allow for quick changes in approaches to meet changing circumstances, and additional data availability for both real-time and after-the-fact analyses will inform future decision making. Two of the most significant developments for performance measurement have been the growth in deployment of automatic vehicle location (AVL) and the MDSS. These technologies could potentially support data collection and processing, respectively, for multiple purposes such as treatment recommendations, route reporting, resource consumption tracking, and inci- dent response, with the level of resolution influencing the extent to which the technologies can assist with decision-making needs (Pletan et al. 2009). Advances in pavement sensors, thermal mapping, and infrared sensors are also improving the ease with which data can be collected and analyzed (Fay et al. 2013). Venner (2011) presented a summary of AVL/GPS technology used by state DOTs and highlighted the following uses of AVL: â¢ Operational/snow-fighting efficiencies â speed, material types and quantities, application rates, temperatures (DE, KS, KY, MD, MN, ND, NY, WA); â¢ Vehicle location and route improvements/efficiencies (IL, KS, KY, NM, NY, WY); â¢ Tracking units in remote areas (NC, NM); â¢ Road conditions over the state, near real time (WA); â¢ Locations of incarcerated crews (MO); â¢ History/daily achievements of sweeping crews (MO); â¢ Contractor vehicle usage while on the clock (VA); â¢ Photos, material used, application rate (NE); and â¢ Management data through the stateâs Force/Precise system (WY). MDSS usage continues to grow. In a survey conducted by the FHWA with responses from 40 state DOTs, 19 states indicated that they were using MDSS either on a statewide basis or partially within the state. Nine other DOTs indicated that their states needed MDSS (Gopalakrishna et al. 2016). The other significant development is the greater availability of road condition information. With every state DOT using some form of traveler information, the heart of the traveler information
30 Performance Measures in Snow and Ice Control Operations systems is the RCRS. The RCRS is often a focal point; it is populated by manual and automated data and information feeds, and it supplies information to various information dissemination mechanisms. Deeter et al. (2014) documented RCRS practices and noted the following effective practices with direct implications on snow and ice performance measurement: â¢ A regular, automated intake of weather data collected by systems external to the RCRS. Through this exchange, the regularly updated weather data are collected on a timed cycle and replace the previous reports. â¢ Generating automated performance measure data using time stamps of manual and auto- mated actions. The growth in the availability and use of weather and road condition data and products is a significant enabler of snow and ice performance measurement. The use of ESS data for main- tenance and traffic decision making has been reported by over 94% of states responding to an FHWA survey (Gopalakrishna et al. 2016). The National Oceanic and Atmospheric Administra- tionâs (NOAAâs) Meteorological Assimilation and Digest System (MADIS) is a national (and in fact global) source of weather observations. MADIS collects data from NOAA and non-NOAA sources, decodes the data, and then encodes all of the observational data into a common format with uniform observational units and time stamps. Quality checks are conducted, and the integrated data sets are stored along with a series of flags indicating the results of the various quality control checks (NOAA 2015). Deeter et al. (2014) highlighted state DOTsâ practices of integrating road weather data into road condition reporting systems and identified the following cases: â¢ In Maryland, the CHART RCRS collects data from the RWIS network to deliver high-level roadway weather information to travelers. In addition, the RWIS data are also automatically added to any incident entered into CHART to maintain a record of road conditions at the time of the event. A unique aspect to the Maryland approach is that attaching weather reports to incidents allows the department to identify patterns between incidents and weather conditions. â¢ In Ohio, basic air and pavement data from the 174 RWIS sites throughout the state are inte- grated into the Buckeye RCRS every 5 minutes. This intake of RWIS data provides additional information to travelers and allows Ohio DOT staff to view the weather information through the RCRS, which is particularly helpful when updating the manual road weather reports. â¢ North Dakota DOT integrates wind speed and radar data into its RCRS. The wind speeds are stored in the RCRS and passed to the departmentâs traveler information website for display to travelers. Both the wind speeds and the radar data are visible to RCRS users to allow them to view current conditions and make decisions about weather pattern changes. â¢ Idaho operates a module of its RCRS that collects RWIS data and assigns a circle of influence to the weather reports based on rules that are mostly dictated by the terrain surrounding the RWIS site (e.g., in mountainous terrain, the circle of influence is smaller than in flat areas). The weather reports are presented on the 511 website together with the circle of influence, allowing website visitors to understand what geographic area is most likely experiencing the conditions. â¢ Iowa DOT operates a module of its RCRS that intakes National Weather Service watches, warnings, advisories, and other similar products in the Common Alert Protocol (CAP). This interface can be used for additional alerts published using the CAP. In the future, increasingly sophisticated data sets should be available from connected vehicles. For example, FHWAâs ITS mobile observations study demonstrated how weather, road condi- tion, and vehicle data can be collected, transmitted, and processed from connected vehicles and used for decision making in transportation and operations for three different state DOT programs (Chapman et al. 2012). Shi et al. (2006) provided a summary of vehicle-based technologies for winter maintenance and noted that the ultimate goal of maintenance is to deliver the right type and amount of
Findings and Applications 31 material in the right place at the right time. Technologies discussed in the report included AVL, surface temperature measuring devices, freezing point and ice-presence detection sensors, salinity measuring devices, visual and multi-spectral sensors, and millimeter wavelength radar sensors. Also affecting performance is the continued evolution of snow and ice control practices. While these are not described here in detail, agency sophistication with snow and ice control practices continues to grow. Advances in technology and improved analysis, operations, and communication tools and techniques are improving the efficiency of snow and ice control operations, and a significant volume of the literature on snow and ice control is dedicated to these topics (Pletan et al. 2009; Murphy et al. 2012; Transportation Research Circular E-C126). 3.5.2 Integration of Weather, Traffic, and Maintenance Operations States are moving toward closer integration of weather, traffic, and maintenance operations. Utah DOT (Ye 2009) and Wyoming DOT (Cox 2010) integrate meteorology, traffic operations, and maintenance through their traffic management center operations. Similarly, the FHWA Pathfinder project (Patterson 2014) seeks to engage the public and the private sector and weather, traffic, and maintenance stakeholders to provide a common shared message of severity, timing, and recommendations. Increased integration can lead to new ways to measure performance. For example, Kwon (2012) developed a procedure to determine the road condition recovery time, which could be used as an estimate of the âbare-lane-regain time,â from the traffic-flow data being collected from the existing loop detectors on freeways. Ohio DOTâs Snow and Ice Performance Evaluator models winter weather events by analyzing speed and weather data (MacAdam 2014). Stronger integration of the weather, traffic, and maintenance operations can also lead to more responsive traffic management strategies such as weather-responsive traffic signal timing (Balke and Gopalakrishna 2013). 3.5.3 Increased Engagement with the Public The ability of transportation agencies and municipalities to communicate effectively and directly with the traveling public continues to grow. Agencies are moving from a one-way communication structure (i.e., providing information to the public, but not receiving direct feedback) to a two-way communication structure (i.e., agency sends and receives information to/from the public) through different versions of âconnected citizenâ programs. A number of state and local agencies are beginning to use social media technologies during inclement weather (e.g., Texas DOT, Kentucky Transportation Cabinet, and the District of Columbia DOT). Through these social media platforms, agencies can share more robust information through the use of photos, GPS-enabled online tracking of snow removal vehicles, and so forth. Additionally, using social media facilitates two-way interaction since the public is able to respond to postings and provide information to the agency. Given the interactive nature of social media platforms, agencies can also use social media to collect information about traveler experiences during snow and ice events by directly asking the public for input and developing tools for citizen reporting. Wyoming, Idaho, and Utah have all developed methods to integrate citizen-reported informa- tion into their traffic management and winter maintenance practices (Deeter et al. 2014). 3.5.4 Prioritization of Asset Management In October 2016, as required by MAP-21, the FHWA adopted a final rule on risk-based transportation asset management plans (TAMPs). The rule requires state DOTs to develop a TAMP that will drive how various maintenance activities are budgeted and evaluated by the agencies (FHWA Office of Asset Management 2017). While maintenance practices may not
32 Performance Measures in Snow and Ice Control Operations change, the greater emphasis on life-cycle costs and clear identification of assets will have an impact on snow and ice performance measurement. 3.5.5 Increased Importance of Transportation Resilience Transportation resilience is a critical part of providing a safe, reliable transportation system. Defined as the ability to prepare for and adapt to changing conditions and withstand and recover rapidly from disruptions (Presidential Policy Directive 2013), resilience includes the ability to withstand and recover from deliberate attacks, accidents, or naturally occurring threats or incidents. Extreme weather, which dictates the level and nature of the snow and ice control response, requires a consideration of how risks and vulnerabilities are managed. While existing performance measures that are focused on recovery of LOS are important to assess, additional performance measures may be required. Bruneau et al. (2003), Tierney and Bruneau (2007), and Nagurney and Qiang (2012) have noted the need to consider the following factors to adequately measure resilience in a system: â¢ Robustness. The ability of systems, system elements, and other units of analysis to withstand disaster forces without significant degradation or loss of performance. â¢ Rapidity. The capacity to restore functionality in a timely way, contain losses, and avoid disruptions. â¢ Redundancy/reserve capacity. The extent to which systems, system elements, or other units are substitutableâthat is, capable of satisfying functional requirementsâif significant degradation or loss of functionality occurs. 3.5.6 Increased Consideration of the Environment From an environmental perspective, the materials used before and during winter weather events for snow and ice control often have negative impacts on water quality, aquatic life, air quality, and vegetation. Fazio and Strell (2011) noted that road salt is a major contributor to elevated chloride levels in ground and surface waters of the northern United States and in many urban streams. Many European countries and Canada have focused on alleviating these nega- tive impacts by minimizing chemical material usage or even switching to less harmful chemi- cals or materials. In the United States, there has been a general shift to anti-icing rather than spraying more traditional materials. Information needed to assess the environmental impacts includes hydrologic and biological data, which are not often collected by state DOTs. Additional monitoring equipment may be needed for data collection in aquatic bodies, the atmosphere, or environments adjacent to the road (Fay et al. 2013). As environmental considerations increase, performance measures that indicate the overall sustainability of programs may become more important (i.e., measures that consider the social, economic, and environ mental impacts). 3.6 Necessary Elements of a Snow and Ice Control Performance Measurement Program The literature review identified several requirements (discussed in the following sections) for establishing a performance measurement program that considers the need for effective monitoring of material, labor, and LOS during adverse weather. It is also evident that clearly stated objectives are critical to a performance measurement framework. 3.6.1 Characteristics of Effective Performance Measurement Programs An effective performance measurement program should be tailored to an agencyâs particular needs, taking into account the agencyâs circumstances and capabilities. Thus, no two performance
Findings and Applications 33 measurement programs are expected to be identical. Nevertheless, some best practices have been developed for establishing performance measurement programs (Grant et al. 2013): â¢ Measuring what matters to the public and decision makers. Public engagement is critical to identifying the issues that residents care about most regarding snow and ice control. Ideally, measures should be selected in consultation with decision makers, the public, experts in the affected fields, and others. This information can then be used as input to identify agenciesâ performance expectations and to establish goals. â¢ Selecting a limited set of performance measures to maintain focus on what matters. A manageable number of measures is likely to be no more than 15âand potentially fewer for agencies with lower levels of resources. Including a large number of measures increases the information collection effort and can reduce focus on the most important considerations. In selecting a performance measure, important questions to consider include: â Does it represent a key concern? â Is the measure clear? â Are data available for calculating the measure? â Is the measure meaningful for the types of services or area? â¢ Coordinating with existing performance measurement and planning processes. Decisions regarding snow and ice control should not be made in isolation from other planning processes. In most cases, snow and ice control is one component of a larger operational framework. Strategies for using performance measures to improve performance must be formulated in the context of this larger framework. â¢ Considering the big picture and trade-offs. Recognize that planning cannot be driven solely by performance data and purely quantitative methods. Considering the long-term investment and asset management context, including factors such as life-cycle costs, risk assessment, and sustainability of solutions, is also critical. â¢ Coordinating and collaborating across agencies. This includes groups within an agency, across agencies, and across multiple partners and stakeholders. In this sense, the develop- ment and implementation of any activity or plan should involve the network of agencies that directly or indirectly influence preparedness and response processes. In the case of snow and ice control, coordinating with safety departments, which often have nuanced strategies involving education, can be critical. â¢ Taking advantage of opportunities to inform and educateâand telling a story rather than just releasing data. Disseminating information and educating personnel should be part of any program since better informed decision makers are more likely to make effective partners in tackling future challenges. Investment in a performance measurement framework helps communicate successes and constraints and explain actions being taken and any external factors that might be influencing performance outcomes. â¢ Dedicating resources. Constant support is needed to maintain, develop, and validate data sets and tools to evaluate and forecast performance measures. Without a consistent framework for tracking progress over time, the value of measuring performance and setting performance targets is limited. 3.6.2 Components of an Effective Snow and Ice Control Performance Measurement Program 184.108.40.206 Ability to Distinguish Between Weather and Non-Weather Event Conditions One capability an agency must have is the ability to distinguish clearly when a weather event started and ended. Although start times might be simple for agencies to determine, some agencies struggle to define an approach for closing a weather event. This difficulty could be due to another storm event that begins immediately, ongoing weather conditions even though
34 Performance Measures in Snow and Ice Control Operations precipitation might have stopped (for example, blowing snow), or continuing response activities. Having a clear policy on what constitutes the start and end of a weather event is essential for monitoring performance because it underpins many of the core performance measures. Some agencies measure overall winter weather hours and non-winter weather hours instead of event start and end times, which might be sufficient for some measures but could preclude event-level summaries. A certain degree of subjectivity could be unavoidable in defining start or end times, and any definition may not capture the full variety of events, event durations, and overlaps. 220.127.116.11 Ability to Determine LOS Before, During, and After an Event LOS is used in operational and performance assessments of facilities across transportation systems to indicate infrastructure status through traffic flow, density values, and other operational metrics. LOS provides a comprehensive scale that is necessary for setting realistic and easily understood performance targets. The ability to determine LOS is critical to several response and segment-level indicators. In some cases, a subjective assessment of LOS may be used when traffic data are not availableâfor instance, the PSIC, which is a visual method used to characterize roadway conditions (Blackburn et al. 2004). Measured travel speeds may be less subjective than visual assessment by field personnel and can be used as a proxy for LOS, but they may be unreliable during low-volume conditions or when there is congestion on roadways. New friction and grip measuring technologies can be used to determine LOS in an objective manner both during and after a storm. In all cases, however, it is important to track the progression of LOS as the event unfolds. 18.104.22.168 Ability to Track Materials, Labor, and Fuel Use All agencies responsible for snow and ice control track material usage. Having historical infor- mation of this type enables agencies to estimate more accurately how much material they can expect to use at an aggregated (e.g., in a given budget cycle) and disaggregated (e.g., for a single storm) level. Given the important implications of material usage, these expectations can then be used to optimize environmental and budgetary/effectiveness programs. Although most agencies have some type of material management system, some agencies have the ability to track material, labor, and fuel use at highly disaggregated levels and in different units. 22.214.171.124 An Existing Technique to Normalize Conditions For snow and ice performance measures to be used effectively in decision making, agencies need a procedure to normalize the measures to event severity or season severity. Without such a procedure, performance measures may be regarded as not reflecting conditions or the unique challenges responders face between regions and differences in weather patterns between seasons. 126.96.36.199 Ability to Obtain Road Condition Reports Road condition reports are typically human observations of driving conditions at various stages of a winter storm and during any after-storm cleanup. Although these reports are subjec- tive, they are valuable to travelers as they plan trips or consider alternatives and to maintenance personnel as they manage snow and ice treatment because the reports are available sooner than other sources of condition data. In addition, road condition reports are useful to performance management in tracking time to bare pavement, time to wet pavement, or time to one wheel track. For example, the Oregon DOT road condition reporting system generates performance reports that include a graph displaying the monthly average delay in notifying the public of incidents and road condition changes. Typically, the measurement and recording of road conditions for road condition reports are performed by plow drivers or other maintenance personnel while out on the roads. Reports are
Findings and Applications 35 often submitted by radio or onboard device. In addition, several agencies have implemented citizen reporting programs, thereby increasing the number of quality reports received. In these programs, citizens are trained to report road conditions using the same definitions as DOT staff. Wyoming DOT launched the Enhanced Citizen Assisted Reporting feature in 2005, and by 2014 it had over 400 citizens sharing information about road conditions and other incidents they observed on the roadways (FHWA 2017a). 188.8.131.52 Ability to Collect Both Weather and Road Weather Observations Weather and road weather data are collected through static and mobile data devices capable of measuring current precipitation, temperature, wind, and other conditions, some of which from several feet below the ground surface to tens of feet into the air (depending on specific sensors deployed). Weather and road data are used in real-time by maintenance personnel. The data are often fed into MDSSs. One example where weather and road weather data are accessed through an MDSS is North Dakota DOT, where staff monitor conditions and manage the response accordingly during a storm event using MDSS, professional experience, and other tools (Western Transportation Institute 2009). Access to real-time weather data enables staff to determine the time of the storm end in order to monitor the time it takes, following a storm event, to reach the desired recovery condition. For performance management, weather and road data are critical when normalizing performance between storms (e.g., measuring time to bare pavement after 2 in. of accumulation versus after 12 in. of accumulation). The Idaho Transporta- tion Department uses a winter performance index, where an SSI is calculated using sensor data (wind speed, surface precipitation layers, and surface temperatures) and is combined with the ice-up duration to establish the winter performance index value (ITS International 2013). Weather and road data collection devices are typically positioned throughout the road network in a pattern based on the needs of maintenance providers, but they can be somewhat constrained by available power, communications, and access issues. Therefore, data collection typically is not available everywhere it is desired. Mobile data collection onboard DOT vehicles helps supplement the data. 184.108.40.206 Ability to Monitor Traffic Impacts Effective snow and ice performance measurement ties directly to the outcomes of the main- tenance response, which in most cases translates to traffic impacts. With growing ITS capabilities in the states and greater private-sectorâcollected speed data, collecting and using traffic data (speeds, delays, travel times, crashes) in winter performance measurement is critical. An example of the use of multiple sources of traffic data is Iowa DOT, which uses data from INRIX, speed maps from Google Traffic, and Waze crowdsourcing reports to identify traffic impacts during and following weather and other roadway events (Iowa Department of Transportation 2017). 3.6.3 Severity Indices The concept of severity is fundamental to performance measurement for winter maintenance. The inputs required for management of winter maintenance are directly correlated to storm severity and to seasonal severity. The following subsections provide a summary of storm and winter severity indices to incorporate into core performance measures, as well as a suggested approach to develop the indices. A winter event is defined as any weather occurrence (with a defined start and end time) that requires resources for preventing, minimizing, or regaining the loss of bare lanes. Winter events can include freezing rain, sleet, snow, drifting/blowing snow, frost, ice/black ice, refreeze, or any combination of these (Minnesota DOT 2016). Agencies responsible for winter maintenance
36 Performance Measures in Snow and Ice Control Operations operations use severity to classify individual winter events and the entire winter season based on event and overall characteristics, such as (total) precipitation and wind speed. Following the approach of what gets measured gets managed, these indices allow agencies to normalize perfor- mance of maintenance activities, enabling comparison between agency regions and seasons. 220.127.116.11 Storm Severity Index The severity of storms has been evaluated in many related but distinct ways, such as by pre- cipitation level, wind speed, area covered, and estimated economic loss. Kocin and Uccellini (2004) developed the Northeast Snowfall Impact Scale to classify severe snowstorms from Virginia to Maine, based on snowfall (4 to 10 in. were defined as moderate, and 10 in. or more were defined as heavy) and the population density of the affected area. Changnon et al. (2006) ana- lyzed historical snowstorms based on precipitation and duration. Changnon (2007) compared the storms across regions and times based on their economic impact, identifying 202 indi- vidual storms between 1949 and 2003 with an associated monetary loss of at least $1 million (2003 dollars). Alternatively, Zielinski (2002) presented a five-level hierarchy for winter storm/norâeaster severity, where Category 1 was the least severe and Category 5 was the most severe, and identi- fied potential storm characteristics and societal impacts for each category. Variability in storm movement (through a duration factor related to the forward speed of storms) and an intensity index were used in developing this scale. The intensity index was developed based on the synoptic weather conditions of the storm, namely the central low pressure difference, the rate of deepening of the central low pressure, and the maximum pressure gradient between the central low and central high pressures of the adjacent anticyclone. There are more comprehensive approaches available in the literature that consider many characteristics of a storm. For example, Idaho DOT calculates an index on a storm-by-storm basis using the following variables: maximum wind speed, maximum surface precipitation, and minimum pavement temperature (ITS International 2013). Nixon and Qiu (2005) developed a now well-known SSI based on a modified list of storm components from Nixon and Stowe (2004) and FHWAâs Manual of Practice for an Effective Anti-Icing Program (Ketcham et al. 1996), and provided levels of severity for each. Essentially, Nixon and Qiu described a storm based on precipitation, road surface temperature (during and after the storm), early behavior (started as rain or snow), and wind speed (during and after the storm). The identified components were used to develop a multiple regression model that yielded an index. To allow for more realistic results, the parameters associated with each component were then modified based on information collected from winter maintenance supervisors. Cerruti and Decker (2011) developed a local winter storm scale based on sustained and gust wind speed, snowfall and ice accumulation, and visibility. The scale is estimated as the weighted sum of the storm elements. Each element has a seven-level scale, ranging from zero (nuisance) to six (catastrophic), but allows for intermediate values through linear interpolation. The authors used previous research on standards and scales to define the different levels (or breakpoints). They combined weights estimated in previous research with multiple regression analysis to create initial weight values for each element. This methodology differentiates between storms with snow accumulations and storms with ice accumulations. If only snow is present, the weights sum to unity, but if both snow and ice accumulations occur, the storm is given weights that sum to greater than unity. The general steps for calculating an SSI based on previously highlighted efforts are: 1. Identify data sources/availability and select storm elements. The first step consists of iden- tifying the data sources (e.g., agency-collected data, RWIS, National Weather Service) and
Findings and Applications 37 describing the appropriate storm event elements to be analyzed and used for the develop- ment of the SSI. Table 4 provides a summary of elements found in the literature. It should be recognized that every location has unique conditions that may need to be included to obtain more robust/realistic results. As such, agencies should use this list as a starting point to which they can add elements if necessary. Furthermore, for this particular methodology, only storm-related elements are recommended for use. To avoid double counting, indicators of resource usage (e.g., amount of salt used) and output/outcomes of winter events (e.g., number of incidents) should not be included because such indicators will be used in developing the core performance measures. Storm Element Description Units* Type Classification of storms based on type and amount of precipitation. Examples: â¢ Freezing rain, light snow, medium snow, and heavy snow; and â¢ Snow and ice. â Temperature (air and road surface) Temperature of the road before, during, and after a storm. This can be measured either continuously or by categories (e.g., warm, mid-range, cold). Dew point or relative humidity could also be considered. Â°F, Â°C Precipitation Amount of snow, rain, or ice that has fallen. It can be measured either continuously or by categories (e.g., moderate, below 10 cm or 4 in., or heavy, over 20 cm or 8 in.). in., cm Rate Intensity of the precipitation within a given time frame. in./h, cm/h Drift Duration of drifting snow. hours, days Wind Wind speed before, during, and after a storm. This can be measured either continuously or by categories (e.g., mild, moderate, strong) and include gusts and direction. mph, km/h, m/s Visibility Road visibility during a storm. This element measures how far a driver can see during a storm. mi, km, m Duration The time a storm affects a particular area. This measure could be disaggregated into the time the storm was occurring and the period during which the effect of the storm was prolonged. h, days Duration Number of hours from a defined start time to an end time. Can be tabulated as âweather-hours.â h, weather- hour Forward speed How fast a storm moves. The slower the storm, the longer its duration in a particular area. mph, km/h, kn, m/s Area covered The area affected by a storm, estimated through a spatial measure or people/mi2, ln- population density. mi, mi2, km2 Storm behavior Qualitative or quantitative measure indicating pre- and post-storm behavior. This measure identifies how a storm begins (e.g., starts as rain or snow, extreme cold) and finishes (e.g., finishes as rain or snow). â Topography Indicator of how the road alignment could affect the impact of a storm. This could be by proxy through qualitative values, such as levels (e.g., 1 to 5) based on a subjective assessment of potential impact of the topography, or through quantitative values, such as proxy by elevation and changes in or average horizontal or vertical alignment. â Timing Qualitative or quantitative measure of severity based on the time of day and day of week a storm takes place, considering its duration in its entirety or partially (e.g., only when it starts). â *Other units may be used if applicable. Table 4. Potential storm elements for development of an SSI.
38 Performance Measures in Snow and Ice Control Operations 2. Decide the storm elementsâ measure units and categories. After selecting the storm elements to be used, the next step is to decide how they should be measured and to identify cut points to categorize the data (if necessary). Regardless of the analysis approach, clear levels (i.e., cate- gories based on specific thresholds) are useful to simplify data that might be too complex to interpret (e.g., meteorological data) or have a wide range (e.g., wind speed). At a minimum, agencies should distinguish low, moderate, and heavy characteristics of a given element (e.g., amount of snowfall to classify precipitation as high). This step also includes the removal or correction of outlier data points. Outlier data points can be described as data that are not representative of the entire population. These points can occur as a result of instrument error or the presentation of abnormal conditions (e.g., unusual weather events for the time of year and location, such as snow in the state of Florida in December). 3. Identify a proper methodology to define the analysis period (i.e., duration) and spatial scope (i.e., areas covered/affected). This step helps determine how much data to collect and how to discern between continuous winter events. 4. Develop merging criteria. Once the elements and their respective units have been selected, agencies must decide how to combine them, taking into account the different types (i.e., quali- tative and quantitative) and units. Various techniques can be used to develop such criteria. Two commonly used techniques are: a. Econometric models (e.g., multiple linear regressions), which use historical information to estimate the contribution of each component to the severity of a storm. The difficulty of this approach lies in identifying initial values for each element, which should be accom- panied by a thorough analysis of historical records. b. Consensus through survey or expert opinion, which can be used to assign initial or final weights to each element. This approach is heavily based on the subjective perspective of the respondents; therefore, the sample selection process should attempt to include a variety of experienced respondents, such as managers, practitioners, and users of the system. 5. Define severity criteria. This step provides the threshold for categorizing severity. This can be done by identifying the various percentiles of the historical distribution or scaling the obtained values to a specified range (e.g., 0â1, 0â10, and 0â100) and dividing it into quantiles. Steps 3 and 4 provide a model to obtain a final SSI when more than one element is selected to characterize the severity of a storm. However, not all elements are required. Sometimes a single element may be selected as a proxy for severity, such as precipitation for a simpler but less robust SSI, as long as the element is strongly correlated with key input, output, and outcome measures. 18.104.22.168 Winter Severity Index As with an individual storm, a winter season can be defined by many of its features, including temperature averages and extremes, snowfall totals, average and highest snow depth, the duration of winter weather conditions, and the aggregated value of its impact (e.g., economic loss). Overall, winter severity tends to be measured by aggregating, averaging, or normalizing one or more features over the length of the season. Some DOTs, such as those of Kansas, New Hampshire, and Massachusetts (MassDOT 2012), have adopted the SHRP WSI (Boselly and Ernst 1993, Boselly et al. 1993). [WSI values are calculated on a monthly basis based on four weather-related factors: minimum daily temperature, maximum daily temperature, snowfall, and the number of days with frost potential (i.e., minimum daily temperature below 32Â°F).] Others either have modified the SHRP index or developed their own, including those of Indiana, Iowa, Minnesota, and Wisconsin (Farr and Sturges 2012). Wisconsin DOT uses the number of snow events, freezing rain events, total snow accumulation,
Findings and Applications 39 and total storm duration. Indiana DOTâs WSI is similar, using these same factors, plus number of incidents, snow depth, storm intensity, and average temperature. Minnesota DOT evaluates the severity of a given winter season by combining information on dew point or relative humidity, wind (speed, gust, direction), frost or black ice, precipitation (type, duration, amount), temperature (air, road surface), cloud cover, blowing snow, and surface pressure (Minnesota DOT 2016). Despite similarities in variables in many states, the complexity of the methods to evaluate WSIs varies broadly by state. For example, Iowa DOTâs WSI is complex and places significance on time by considering the duration (instead of occurrence) of each winter variable (Carmichael et al. 2004). The variables include number of wet snow events, number of dry snow events, number of freezing rain events, snowfall in inches, hours of wet snow, hours of dry snow, hours of mixed precipitation, hours of freezing rain, hours of blowing snow, hours of sleet, average of lowest temperatures during wet snow events, average of lowest temperatures during mixed pre- cipitation events, average of lowest temperatures during freezing rain events, average of lowest temperatures during sleet events, average of lowest temperatures during dry snow events, and average of lowest temperatures during blowing snow events. Other approaches of note are those of Suggett et al. (2006) and Mayes Boustead et al. (2015). Suggett et al. developed two WSI models to estimate expected harshness of winter seasons. Normalized salt usage (tons/ln-km/day) was used as the main indicator, accounting for differ- ences in road network and the number of days in the observation period. One model was based solely on data from the Meteorological Service of Canada (MSC), and the other model combined MSC data with RWIS data. The result was an index based on the predicted values, which were then scaled between 1 and 100 given the minimum and maximum obtained values, respectively. Mayes Boustead et al. (2015) developed an Accumulated Winter Season Severity Index (AWSSI) based on commonly available dataâmax/min temperature, snowfall, and snow depth or precipi- tation. This methodology estimates daily scores from values assigned to temperature, snowfall, and snow depth thresholds. The daily scores are accumulated through the winter season, allowing for a continuous severity analysis during the season. After normalization, the AWSSI could be used to compare a winter season across regions to determine which site had a more severe winter relative to its own climatology. However, this method does not take into consideration wind (e.g., wind chill, blowing snow), mixed precipitation, or freezing rain, and it would not work in a climatology that experiences year-round winter. A general approach to develop a WSI that builds on previous research is as follows: 1. Identify a proper methodology to define the analysis period (i.e., duration) and spatial scope (i.e., areas covered/affected). This step helps determine how much data to collect and how to discern winter events within a season. 2. Define reporting unit(s) of elements. This step ensures that all subsequent efforts are designed to correlate to and reach the desired objective(s). The units of analysis are usually defined as aggregated, averaged, or normalized measures of storm elements or SSIs. As for an SSI, indicators of resource usage and output/outcomes of winter events should not be included when creating the WSI because these will be used in the development of the core performance measures. 3. Develop merging criteria. As for an SSI, agencies must decide how to combine the selected elements, taking into account the different types (i.e., qualitative and quantitative) and units. Various techniques can be used to develop such criteria. a. Econometric models (e.g., multiple linear regressions) use historical information to esti- mate the contribution of each component to the severity of a winter season. The difficulty of this lies in identifying initial values for each element, which should be accompanied by a thorough analysis of existing historical records.
40 Performance Measures in Snow and Ice Control Operations b. Consensus through survey assigns initial or final weights to each element. This approach is heavily based on the subjective perspective of the respondents; therefore, the sample selection process should attempt to include a variety of experienced managers, practitio- ners, users of the system, and any other important (or affected) stakeholders. c. Aggregation of a single variable over the winter period is also a simple and effective way to estimate overall winter severity and is suggested when agencies already have a record of SSIs of all storms during the winter period. 4. Define severity criteria. This step provides the threshold for categorizing severity. Most approaches found in the literature depend, ultimately, on defining breakpoint(s) of accu- mulated severityâfor example, answering the question, âHow many days with more than 10 in. of snow defines a winter season as mild, moderate, heavy, or extreme?â This can be done through identifying the various percentiles of the distribution or scaling the obtained values to a specified range (e.g., 0â1, 0â10, and 0â100) and dividing it into quantiles. Another approach is to develop an algorithm by which points are assigned on a daily basis based on observed weather conditions; these daily values are then summed to create monthly or seasonal scores. Steps 3 and 4 provide a model to obtain a final WSI when more than one element is selected to characterize the severity of a storm. However, if only one element is selected, this element should be normalized to obtain a less biased WSI. In the example for SSI, if precipitation is selected as the sole element of analysis, it could be standardized by coverage, yielding a WSI measured as average inches of snow per lane mile per day for heavy winter areas and average inches of snow per lane mile per month for milder winter areas. 3.6.4 Data Requirements for Performance Measures Table 5 details some important data elements that should be collected to successfully estimate performance measures. Ongoing Data Collection Throughout the Winter Season Pavement Surface Condition Status Definition: Typically the percentage of a road segment that is bare of snow; alternate reporting methods include percent bare pavement in wheel tracks, grip, and LOS. Data source: Reports from agency personnel in the field or citizen reporters. Improvements for additional, better quality data: Training for reporters to standardize visual reports; provision of electronic reporting (versus verbal reporting); expansion of reporters to plow drivers, other agency personnel, law enforcement, and citizen reporters. Limitations: Data are subjective and may be inconsistently reported by various individuals; frequency for recording this measure may be low depending on number of individuals reporting data and expectations for reporting, plow pass frequency, and electronic data collection capabilities. Fatal Crash Records Definition: Documentation of crashes (typically within 30 days of the crash) where at least one individual was killed as a result of adverse weather conditions. Data source: NHTSA Fatality Analysis Reporting System (FARS); crash records maintained by state transportation agency safety management group or law enforcement agencies; transportation management center (TMC) incident records. Improvements for additional, better quality data: Availability of causal factors, location, time, and number of individuals killed in a crash facilitate more comprehensive measures; details typically logged by law enforcement personnel may not be consistent or clear; detailed crash records may not be available electronically; detailed crash records may be difficult to obtain due to privac y concerns. Limitations: TMC incident records may not log all crashes or their severity or encompass all areas within the desired region. Table 5. Data element requirements to calculate performance measures.
Findings and Applications 41 Ongoing Data Collection Throughout the Winter Season Injury Crash Records Definition: Documentation of crashes where at least one individual was injured as a result of adverse weather conditions; typically scaled for severity of injuries; finalized 30 days after the crash to remove fatalities occurring during that period. Data source: Crash records maintained by state transportation agency safety management group or law enforcement agencies; TMC incident records. Improvements for additional, better quality data: Availability of causal factors, location, time, and number of individuals seriously injured in a crash facilitate more comprehensive measures; details typically logged by law enforcement personnel may not be consistent or clear; detailed crash records may not be available electronically; detailed crash records may be difficult to obtain due to privacy concerns. Limitations: TMC incident records may not log all crashes, their severity, or encompass all areas within the desired region; comprehensive records of injury crashes more difficult to obtain than fatal crashes; identifying seriously injured individuals who were involved in a fatal crash may add a challenge. Time Stamp for Start or End of Winter Event Definition: The estimated time when winter precipitation began or ended for a given road segment. Data source: RWIS snow accumulation data; weather radar observations; reports by agency personnel in the field or citizen reporters. Improvements for additional, better quality data: Use of more data sources to derive more precise estimates for specific road segments instead of one time stamp that applies to the entire region. Limitations: Minimal; use of only one time stamp for a region increases inaccuracy of the measure. Time Stamp for Observation Definition: The time when the reported data were observed, as logged in real-time by automated reporting systems or manually by individuals. (This may be different from the time stamp of the logged report since reports may not be logged in real-time.) Data source: Typically accompanies the logged data as a separate field. Improvements for additional, better quality data: Use of automated data sources and real-time reporting can improve accuracy of the time stamp. Limitations: Minimal; delayed reporting of manually logged data may not have accurate time stamps for observation. Traffic Volume Definition: Actual or estimated counts of traffic on a road segment during a weather event or over the winter season to calculate VMT. Data source: Continuous count station data; average annual daily traffic (AADT) estimates adjusted by seasonal factors. Improvements for additional, better quality data: Increased use of continuous count station data and deriving event-specific factors from actual counts to identify the percent of traffic volumes versus typical conditions to apply to volume estimates in order to derive event -specific volume estimates improves the precision of measures. Limitations: Seasonal volumes include nonevent data that reduce precision of measure; volume estimates for roadways lacking continuous count stations may not be accurate, making seasonal or event-specific estimates even less accurate. Weather Event Information Definition: Data specific to weather events to determine storm severity may include rate of precipitation or total precipitation, an accumulation-related threshold, or a friction-related threshold. Data source: Typically, logged RWIS data; weather radar observations or reports by agency personnel in the field or citizen reporters may be an alternate source. Improvements for additional, better quality data: A broader network of RWIS sites will assist with improved understanding of localized weather activities. Limitations: Minimal. Road Closure Information Definition: Information regarding the mileage and duration of road closures to calculate a closure- related threshold. Data source: Typically reported by agency personnel in the field. Improvements for additional, better quality data: Use of automated reporting may improve accuracy of the time stamp. Limitations: Minimal; manually reported data may not have accurate time stamps. Table 5. (Continued). (continued on next page)
42 Performance Measures in Snow and Ice Control Operations Data Collected Once or at Beginning of Winter Season Customer Satisfaction Levels Definition: Assessment of traveler satisfaction with snow and ice response. Data source: Periodic surveys of travelers either conducted seasonally or after selected events. Improvements for additional, better quality data: To mitigate expenses, surveys can be linked from existing 511 and traveler information sources (such as agency websites, apps), but note limitations. Limitations: Survey information collected from traveler information sources may result in some biases based on the nonrepresentative nature of the respondents. However, more robust sampling approaches may be cost prohibitive. Winter Maintenance Resource Use Definition. Actual recorded use of winter maintenance resources (fuel, labor, and material) during a season at various levels of disaggregation (by region, by event). Data source: Typically recorded as part of agency or a contractor material management system. Improvements for additional, better quality data: Event-level reporting of material use can be useful to assess correlation of levels of service with material. Limitations: Generally, no limitations in this type of data. Agencies might have better records of amounts of resources rather than the cost of such resources (especially for labor). Road Weather Data Definition: Weather and road weather data required to adequately describe the conditions of the snow and ice response. Includes at least the following: precipitation start and end times, precipitation total amounts, and precipitation rate (in./h). Additional sensors can provide friction measures like grip factor. Data source: RWIS. Improvements for additional, better quality data: While some mobile data collected from fleets can provide additional information, a fixed RWIS site is likely the best bet for robust time-stamped weather information that can be used for performance measurement. Limitations: RWIS coverage at most agencies is limited, and significant gaps in coverage mean that weather information collected is only representative of overall conditions on the system. Performance Target or Threshold Definition: The goal an agency seeks to achieve for each performance measure and measured service level; needed to calculate performance measures regarding meeting service-level thresholds; may vary based on the road classification or priority, weather event severity, day of week, or time of day. Data source: Agency personnel or decision makers. Improvements for additional, better quality data: Increased precision with variations to make the measure more suitable for each roadway segment; adjustments to be more realistic, measurable, and intuitive. Limitations: Unrealistic or poorly set thresholds may not lead to a good indication of performance. Road Segment Information Definition: Priority for calculating specific measures or setting different performance thresholds; may vary by road classification or for rural versus urban areas. Data source: Priority may be set based on standard functional classifications or groupings of several classes. Improvements for additional, better quality data: Increased precision with variations to make the measure more suitable for each roadway segment based on winter maintenance operations priority. Limitations: Minimal. Speed Data Definition: Average traffic speeds to determine the degree of mobility degradation or decreased level of service during and after a winter weather event or to help identify a return to normal mobility following a winter weather event. Data source: May be logged from agency loop detectors or procured via a third-party provider such as INRIX. Improvements for additional, better quality data: Probe data from third parties can greatly expand coverage from agency systems. Limitations: Less-traveled routes and rural areas are less likely to have loop detectors or reliable probe speed data from third-party providers; reduced traffic during winter storms, particularly during non-peak hours, may have greater variability, and it may be difficult to accurately assess conditions. Ongoing Data Collection Throughout the Winter Season Table 5. (Continued).
43 The following sections describe how agency missions and goals drive performance measurement through the identification of operational objectives. Seven operational objectives were identified and, correspondingly, seven performance measures are described in detail. More information on the analytical approaches can be found in the guide provided in Part II. 4.1 Impact of Mission and Goals on Performance Determining what constitutes effective snow and ice control performance by an agency is linked to the agencyâs mission and goals. As illustrated in Figure 5, an agencyâs mission and goals direct operational objectives, which are used to set performance standards. These standards drive the identification and development of performance measures. Therefore, it is necessary as a first step for the agency to review its stated mission and goals and how they relate to snow and ice control. Snow and ice control is one part of a larger agency mission of achieving safe, reliable, and sustainable operations. The relationship of snow and ice control to an overall agency mission and goals drives the investment in this area and the corresponding emphasis on performance measurement. Stated missions and goals also determine the constraints on snow and ice opera- tional practices and policies. For example, an agency with either an implicit or explicit goal of never closing roads will have a different set of performance expectations than one where road closures are used widely. In large part, the mission and goals are driven by the type of jurisdiction the agency manages and the public-sector role in snow and ice control. Table 6 identifies key elements in agency missions and goals and how they drive performance expectations and, ultimately, the selection of performance measures. 4.2 Operational and Maintenance Objectives for Snow and Ice Control Once the mission and goals of an agency are reviewed, operational and maintenance objec- tives can be developed or refined to meet these goals. Operational objectives directly correlate to performance measures; it is necessary to identify a means for measuring the achievement of the objectives. The achievement of the performance standards then becomes the primary performance measure for the agency. These objectives drive the nature and cost efficiency of the response. An agency that sets a high LOS performance standard during an event could incur more cost than an agency that has a lower or no LOS performance standard. Similarly, recovery objectives (e.g., how quickly roads/systems will be cleared) drive the level of response activity. Where possible, operational objectives should be defined so that they are quantifiable through one or more of the measures listed in the following subsections. C H A P T E R 4 Development of Performance Measures Mission and Goals Operational Objectives Performance Standards Performance Measurement Direct Set Drive Figure 5. Link between goals and performance measurement.
44 Performance Measures in Snow and Ice Control Operations Question Options Impact on Performance Expectations and Measurement How important is snow and ice control to the agencyâs mission? Agencies are likely to have a dedicated winter maintenance program. Existing protocols allow for ability to focus LOS during events. With snow and ice removal a major portion of the budget, there is a strong emphasis on efficiency. Travelers are used to snow and ice conditions and have high performance expectations during such events. Medium priority. Snow and ice are a factor but not the dominant concern. Agencies are likely to have a maintenance program that includes defined roles for winter maintenance, but staff may be drawn from different roles/functions. Likely to not use as many resources as high-priority agencies do. High-severity events cause a challenge for agencies to respond and maintain LOS. However, recovery from such events is important. Travelers may not be as used to snow and ice conditions. Expectations about driving conditions may be unfounded. Agencies typically have ad hoc responses to snow and ice events, which are rare. With little or no dedicated resources, agencies tend to focus on recovery from the event, knowing that such an event would result in higher costs than expected. Is there a handbook/policy for snow and ice control? Yes A written policy allows for more consistent use of winter maintenance tactics across an agencyâs different districts/regions. Policies for closures, vehicle restrictions , and messaging directly influence operational objectives for the agency that in turn determine performance. No Agencies without a written policy may end up with ad hoc approaches across the different districts/regions. Greater effort is needed to develop a common, agreed-upon set of performance measures for agencies of this type. What is the nature of the jurisdiction that the agency manages? Mostly urban Snow and ice control in large urban areas presents both opportunities and challenges for performance measurement. Measuring reliability of travel, especially around important commuter sheds and peak hours (a challenge in normal weather) is critical during snow and ice events. Distinguishing the impact of weather from the regular spatial and temporal extent of congestion is a challenge. Agencies typically have high levels of instrumentation, monitoring and detection systems, and availability of probe data to support more detailed performance measurement. Mix of urban, suburban, and rural Agencies with a mix of urban, suburban, and rural facilities are likely to require a prioritized approach to response and recovery, with clear identification on levels of priorities on their different facilities. These different priorities can result in significantly different performance targets within a region. Largely rural Largely rural areas have limited detection but also low volumes. They are likely geographically large and require a lot of time to respond to conditions. Expectations of high levels of service during snow and ice events may be unrealistic, but recovery from these events may be more critical. Recovery of roadways that are lifelines for rural connectivity becomes a priority. What is the public-sector role in snow and ice response? Public sector only Performance and cost control are still important, but the ability of a public agency to ramp up or ramp down resources may be limited. Contracted services (fully or in portions of the agency) When snow and ice control is handled largely through contracts, there is a greater need for clear performance specifications around response and recovery and monitoring/reporting of such specifications to enable contractual compliance. The public-sector role in oversight also requires a stronger reliance on performance data. What type of operations does the agency manage? Freeways only Agencies are likely to have high levels of service and capabilities to manage snow and ice response on these roads since they are likely to be the highest priority. Arterials only Other aspects of levels of service, such as considerations of sidewalks, bike infrastructure, and signal progression, may drive performance expectations. Mix of roadways Agencies are likely to have a varying mix of performance requirements for their roadway types. Transit only Snow and ice conditions can greatly affect surface transit use. While similar concepts of performance measures apply for both transit and roadways, the definitions and the implementation of the measures may vary. What type of weather- response traffic management strategies are in use by the agency? Low level of operational strategy use At a low level of operational strategy use, an agency might still be influencing demand on travel through messaging. High level of operational strategy use At high levels of operational strategy use, agencies seek to actively manage the roadways before, during, and after an event. From a full suite of advisory and control strategies, agencies can set restrictions on speed, lanes, and vehicles; can open/close lanes/roadways; and can actively support demand management (route, time, mode choices). In such cases, it is important to revisit the expectations of the performance measures in light of these operational strategies. For example, a static, speed-based LOS or recovery criterion is not useful in a context of variable speed limits. High priority. Snow and ice conditions routinely occur and need to be managed, which is a primary task of the agency during winter. Low priority. Snow and ice conditions are not a frequent occurrence in the agencyâs jurisdiction. Table 6. Impact of agency mission and goals on performance measurement.
Development of Performance Measures 45 For snow and ice control, outcome-related operational objectives are ideally defined within the seven categories identified in Figure 6. Each of the seven objectives sets a performance standard that can be measured and reported. These objectives cover pre-storm preparations, during-event conditions, and post-event recovery. Achieving all the objectives may require accounting for the increased resources needed to meet higher LOS and recovery objectives. Implied in the objectives is a definition of an event. Agencies must have the ability to distinguish clearly when a weather event started and ended and when conditions returned to normal. While start times may be easy to determine, end times of weather events may be more difficult to define for some agencies. In addition, start and end times may be difficult to define for events such as icing events, multiday events, start-and-stop events, and overlapping events. If such events are infrequent, they can be treated as exceptions, but if they are common, an agency can apply start and end time criteria that are based on field personnel observations and are long enough to account for the impact of the event. 4.2.1 LOS During Event Operational objectives in this area correspond to maintaining travel during the event at an acceptable level. The âacceptabilityâ may vary depending on the type of roadway and type of agency. In some cases, an agency might not want to specify an LOS during the event, depending on the nature of the operations, which is likely when an agency is willing to close roads as part of the winter response or if transit service is suspended. The reasons for defining an LOS during an event are: â¢ It establishes an expectation based on severity and type of roadway on what the agency is expected to provide, and â¢ When contractors are used, it allows for monitoring of the response quality more effectively. Collecting LOS data during an event is a complex task, and data are likely to be available only for selected segments. The different approaches are listed in Table 7. While none of them are perfect, an agency can make progress by starting with a few priority segments. In addition, defining the LOS depends on the nature of the operational strategies involved. For example, speed-based LOS definitions are likely not well suited for an agency that relies extensively on variable speed limits. In such a case, the spread of speed distribution might be a better LOS measure to track. Figure 6. Categories of operational objectives.
46 Performance Measures in Snow and Ice Control Operations Ways to Define LOS During an Event Approach Comments Maximum accumulation during the course of the event (Measured). This is calculated for sections where there is an RWIS station present to observe continuous accumulation only. This is limited by the number of RWIS sites deployed and could be a good option for agencies with large-scale RWIS deployment. (Estimated/reported). This LOS is calculated for sections where field personnel reports are available during periodic intervals. Obtaining an estimate of accumulation from frontline personnel is difficult. However, such estimates could be collected from supervisory staff in the field. Maximum allowable drop in roadway friction (alternatively, allowed roadway condition during the event) (Measured). This is calculated only for sections where there is an appropriate sensor present to report continuous grip factors or other friction readings. This is limited by the number of sensors deployed but could be a good option for agencies with large-scale deployment. (Estimated). This is calculated based on field personnel reports during the event. Estimates of road conditions can be qualitatively provided by tools such as PSIC. However, getting sufficient reports during an event may be problematic. Maximum allowable drop in speeds (Segment-based) This is based on point measurements of speed through agency-owned sensors and is possible where such sensors are deployed. (Probe-based) This is based on getting travel speeds on a significant portion of the network through probe data using third-party providers. Table 7. Approaches to collect LOS data during an event. Variation of LOS Objective by Severity and Roadway Type Low Medium High Priority Roads âAâ Maximum LOS Objective Priority Roads âBâ Priority Roads âCâ Minimum LOS Objective Increasing Event Severity Decreasing Priority Figure 7. Variation of LOS objective by severity and roadway type. Once an agency picks an approach, it needs to specify the LOS objective based on the severity of the event and the priority of the roadway, because providing the same LOS for all types of events and all roadways cannot be expected. Figure 7 illustrates the relationship between severity and priority of roadway types. 4.2.2 Recovery from Event This objective focuses on how quickly an agency recovers from the event to a normal condi- tion determined based on historical averages. The sooner the recovery the better, but a quicker recovery also implies a higher level of response resources. Also, all parts of an agencyâs system
Development of Performance Measures 47 are not likely to have the same recovery targets. Objectively defining what constitutes ârecoveryâ or is a return to normal is the primary challenge in measuring this objective. Figure 8 illustrates the difference between the concept of âLOS during an eventâ (identified in Section 4.2.1) and this objective. Recovery from an event is typically specified by the time to reach a specific condition after the event. The attainment of the condition can be measured in the following ways. â¢ Return to normal or specific pavement conditions (reported by field personnel). â¢ Return to normal or pre-event roadway friction/grip factors. â¢ Return to normal speeds as measured by sensors or probes. Determination of normal speeds should factor in any variable speed limits, or historical data should be considered in their definition. 4.2.3 Reliability The travel reliability during event measure tries to determine an acceptable drop in travel time reliability due to a snow or ice event. It provides trip- or route-based assessment of impact through travel reliability measures as opposed to segment-level LOS. Reliability-based operational objectives may be used to focus on certain priority trips/corridors and periods during snow and ice events. The measure is best used for understanding the trip-level impacts on certain key corridors and periods. Travel time data can be obtained from the National Performance Management Research Data Set (NPMRDS; https://npmrds.ritis.org/analytics). The NPMRDS is made available to state DOTs and metropolitan planning organizations (MPOs) by FHWA through acquisition of travel time data from a private vendor. The NPMRDS is obtained from anonymous GPS probe data from a wide array of commercial vehicle fleets, connected cars, and mobile apps. NPMRDS uses travel times to measure, monitor, and report the health of road networks. It is considered the baseline data set for meeting federal congestion and performance reporting regulations. As a first step for the analysis, base average travel times void of adverse weather for the entire winter season are computed on the selected corridors. These may be used in comparative analysis with the adverse weather events. Historical travel time data from multiple years should be analyzed to derive base average travel times for all periods of the winter season. The travel times can be aggregated to analysis periods of 5- to 15-min intervals, depending on the desired precision for reporting the reliability measure. Extensive data mining of the NPMRDS is needed to derive the base travel times for the entire span of the winter season. The base conditions should be computed for each day and all times of day during the winter season. In computing the base travel times, the historical data biased due to presence of extreme weather and incident conditions should be excluded. After the base travel times are established, the average travel times over the selected corridors are computed for the adverse weather events in the analysis year. For post-event performance measurement of the maintenance activity over the selected corridors, the average travel times Event Start Time Event End Time Return to âNormalâ Figure 8. LOS and recovery objectives.
48 Performance Measures in Snow and Ice Control Operations over the event horizon should be computed from the NPMRDS aggregated to the same analysis period (5 to 15 min) as the base travel time data. The duration of a storm event can be a few hours during a single day or can span multiple days. The average base travel times are computed for the exact same duration (day and time) as the storm event from the historical data. Because NPMRDS probe data are sparsely available during nights and weekend off-peak hours, especially during storm events, they can be supplemented with other reliable sources of travel time data to increase the coverage of travel time data during storm events. The PTI and BI are often used for travel time reliability evaluation. The PTI signifies the total travel time the traveler would need for on-time arrival. The BI illustrates the buffer time that a traveler should add to the average travel time to ensure on-time arrival. SHRP2 Report S2-L03-RR-1 (Margiotta et al. 2013) suggests using the BI as a secondary reliability metric since it can be erratic and unstable. These indices can be computed using the following formulations: =PTI 95th percentile travel time free-flow travel time ( )=BI 95th percentile travel time â average travel time average travel time The PTI and BI should be computed for the event duration both for the exact same duration base travel times and the travel times during adverse weather events. Figure 9 shows travel times over a hypothetical corridor during normal weather and adverse weather. Table 8 illustrates computation for the BI and PTI. The BI and PTI during adverse weather events over the selected corridors should be within acceptable differences compared to the indices in absence of the adverse weather. In this case, if the acceptable differences were 30% for PTI and 10% for BI for the type of severity, this particular route would have failed the operational objective and performance standard. 0 5 10 15 20 25 30 6: 00 6: 05 6: 10 6: 15 6: 20 6: 25 6: 30 6: 35 6: 40 6: 45 6: 50 6: 55 7: 00 7: 05 7: 10 7: 15 7: 20 7: 25 7: 30 7: 35 7: 40 7: 45 7: 50 7: 55 8: 00 8: 05 8: 10 8: 15 8: 20 8: 25 8: 30 8: 35 8: 40 8: 45 8: 50 8: 55 9: 00 9: 05 9: 10 9: 15 9: 20 9: 25 9: 30 9: 35 9: 40 9: 45 9: 50 9: 55 A VE RA G E TR A VE L TI M ES (M IN .) ADVERSE WEATHER EVENT HORIZON Normal Weather (left) Adverse Weather (right) Figure 9. Travel times over a hypothetical corridor: normal weather and adverse weather.
Development of Performance Measures 49 4.2.4 Safety-Related Objectives Important objectives of winter maintenance activities are to improve safety and reduce the risk of fatal crashes for all transportation system users in the midst of adverse winter weather. Crash data can provide valuable insight into the success of snow and ice control operations. Crashes, and especially fatal crashes, are an infrequent phenomenon. Further, travelers may choose to defer trips, leading to reduced demand on the system and potentially reducing crash rates during an event. However, when adjusted for the reduced volume, crash rates during snow and ice events are higher than on dry days. While many of the crashes that occur during winter weather are minor, objectives related to fatalities and serious injuries are consistent with the MAP-21 Safety Performance Manage- ment Measures Final Rule (FHWA 2013). The rule established five performance measures as the 5-year rolling averages for (1) number of fatalities, (2) rate of fatalities per 100 million VMT, (3) number of serious injuries, (4) rate of serious injuries per 100 million VMT, and (5) number of nonmotorized fatalities and nonmotorized serious injuries. Agencies are expected to develop methods to calculate the measures annually and also to set targets. Fatality measures also support overall highway safety improvement programs, including initiatives like Vision Zero, that are being considered in different parts of the country. Other safety objectives for snow and ice operations that may be included but are harder to set are for secondary crashes during weather events and incident clearance times during weather events. Neither is as intuitive or clear-cut as fatalities and crashes. Defining secondary accidents continues to be a challenge, despite the significant role agencies have in incident management. 4.2.5 Level of Customer Satisfaction The perception of roadway users is a valuable assessment measure since it allows agencies to better understand the needs of travelers in moments of distress. The objective of customer satisfaction can be used to measure the impact of current and future operational practices. Direct customer feedback, while subjective and expensive, can be helpful in ensuring support for snow and ice control operations. With recent developments in apps and smartphone technology, new capabilities are starting to emerge for effective and cost-effective citizen engagement. The customer satisfaction objective can influence how other operational objectives are set. 4.2.6 Efficiency-Related Objectives Winter maintenance operations have a significant economic impact, both from an agency and a roadway user perspective. Travelersâ economic losses due to winter events are mainly tied to safety and mobility issues, such as crashes and unexpected delays. For example, Sasha and Young (2014) estimated that road closures on I-80 in Wyoming had an economic impact Performance Indicators Normal Weather Adverse Weather Difference Free-flow travel time (min) 13.82 13.82 â Average travel time (min) 24.7 32.02 7.32 95th percentile travel time (min) 32.1 42.9 10.8 PTI 2.32 3.10 0.78 (34%) BI 0.30 0.34 0.04 (13%) Table 8. Performance indicators.
50 Performance Measures in Snow and Ice Control Operations of around $11.7 million per 8-h closure for the freight industry. Road closures, however, can help agencies avoid elevated incident costs and the costs associated with rescuing injured and stranded motorists. Another key element is the lost economic activity associated with snowstorms. A study quantified the economic cost of 1-day shutdowns due to snowstorms in 16 states and two Canadian provinces and estimated impacts in the $300 million to $700 million range for a 1-day shutdown (IHS Global Insight 2014). Although estimating the cost of winter main- tenance is straightforward, estimating the economic benefits is challenging, and calculating the economic impact is even more difficult, with no consensus on the best approach. Cui and Shi (2015) suggested that the economic benefits of winter maintenance can be estimated by evaluat- ing energy savings in fuel costs, deduced wage loss from work absenteeism, reduced production losses, and reduced delays in shipment of goods. In general, overall system efficiency objectives from a user perspective are captured by the previously described objectives. However, agencies are likely to have efficiency-related objectives that help ensure that the cost of maintenance operations is consistent with the severity of the event and the season. The more severe the season, the higher the expected cost of maintenance. 4.2.7 Environmental StewardshipâRelated Objectives Material resources have significant infrastructure and environmental impacts, such as in the case of chloride salts. Managing the environmental impacts of winter maintenance operations can be accomplished through appropriate salt management (Fay et al. 2013), the environmental impact of which has been studied in depth (Roth and Wall 1976; Hawkins 1971; Paschka et al. 1999; Ramakrishna and Viraraghavan 2005; Fay and Shi 2012). Research confirms that repeated applications of chloride-based deicers (salts) and abrasives adversely affect adjacent soil and water bodies, thereby affecting vegetation, aquatic biota, and wildlife (Buckler and Granato 1999; Levelton Consultants 2007; Venner Consulting and Parsons Brinckerhoff 2004). Abrasives such as sand, which are used to provide temporary traction improvement on icy surfaces, have been found to clog stormwater catch basins, harm aquatic animals, trigger respiratory problems, and remain in the environment even after cleanup (CTC and Associates and Wisconsin DOT 2009; EPA New England 2005; FHWA Office of Operations 2017). An accident analysis of ice control operations in four states (New York, Illinois, Minnesota, and Wisconsin) determined that the rate of all traffic accidents before salt spreading was about eight times higher than that after salt spreading (Kuemmel and Hanbali 1992). Therefore, agencies should strive to achieve a balance between reaping the societal and economic benefits of spreading salt and mitigating the negative environmental impacts of doing so. Some studies have even attempted to quantify the environmental costs of traffic accidents. One study esti- mated that congestion costs, including travel delay, added fuel usage, and adverse environmental impacts, totaled $28 billion in 2010 (Blincoe et al. 2015). Another study modeled various crash scenarios to quantify the potential environmental harm caused by accidents in terms of added fuel emissions during increased idling in the ensuing congestion and emissions from motor fuel tank leakage as a result of collisions (Hagemann et al. 2013). Although it is difficult to indirectly quantify the potential environmental harm of not spreading salt, it is an important factor to weigh given the increased propensity of crash occurrences when proactive deicing measures are not implemented (Kuemmel and Hanbali 1992). Achieving other operational objectives requires the use of appropriate amounts of salt. However, if an agency is able to model and benchmark salt usage based on historical trends and severity, an environmental stewardship objective could be tied to the ability of the agency to maintain the level of salt use as dictated by the model. The stronger the correlation between the severity and salt use, the better the ability of an agency to monitor and report on this objective.
Development of Performance Measures 51 4.3 Linking Performance Measures to Operational Objectives Seven performance measures were identified to support monitoring the seven objectives. Each performance measure includes many variations that are related to agenciesâ capabilities. Table 9 shows how the operational objectives relate to performance measures. The identified performance measures support a wide variety of agency capabilities, types, and functions. Together, these measures provide a balanced view of outcomes and impacts associated with snow and ice control operations. Table 10 highlights the efficacy of these measures in sup- porting agency and contractor decision making. The performance measures are described in the following sections. Part II provides more information on each measure, including examples and suggested analytical approaches to estimate them. 4.3.1 Percent of Time Road Segments Meet Agency-Defined Level-of-Service Thresholds During Winter Storms This event-based measure assesses whether service-level thresholds were maintained during the event, thereby measuring agency performance of winter maintenance activities. Service-level thresholds vary by type of roadway and winter severity but are often set by an agency as a surrogate for crash risk and to some extent mobility needs. Service-level thresholds are also often used for monitoring contractor performance. 22.214.171.124 Measure Definition This performance measure assesses whether one or more preestablished service-level thresholds were maintained during the event. A service-level threshold defines the acceptable road conditions during an event. The percentage of the event duration during which the service-level threshold was met is determined. Therefore, agencies must be capable of monitoring the service-level threshold for the duration of the event. The measure an agency selects may vary based on the availability of data and resources required to calculate the data, suitable local conditions, and operating policies and constraints. Objective Identified Performance Measures Maintain level of service during event Percent of time road segments meet agency-defined level-of-service thresholds during winter storms Meet recovery criteria set by agency Percent of segments meeting time to regain or recover to acceptable criteria for agency-defined segments after the end of event Meet reliability targets for Percent of trips within accepted difference between measured travel time specific routes Support safe operations of the roadway Five-year rolling average of number of fatalities and injuries during a winter season Meet customer satisfaction ratings Customer satisfaction ratings for snow and ice response Support efficient use of resources to meet operational objectives Cost of snow and ice control to meet established performance criteria for a given winter severity Support environmental stewardship goals by optimizing material use Agency within acceptable difference between expected and actual use of salt and other materials in a season index and additional expected travel time index for snow and ice events for selected routes Table 9. Relationship between operational objectives and performance measures.
52 Performance Measures in Snow and Ice Control Operations Objective measures of service quality during an event, such as measured accumulation amounts or measured grip factors, are preferred. Service-level thresholds can be stated in one of the following forms: â¢ Road conditionârelated. Based on assessment by field personnel into LOS A to F. â¢ Accumulation-related. Based on snow and ice accumulation (e.g., hours where accumulation was below maximum allowable depth target during an event) as measured either by field staff or by spot-specific RWIS sites. â¢ Friction-related. Based on grip factor (e.g., hours of grip factor rating of roadway being greater than target during an event) as measured at specific sites along the roadway. â¢ Travel speedârelated. Based on measured travel speeds (e.g., hours where percentage of average travel speeds were above a minimum expected speed during an event), recognizing that speeds are expected to drop during weather events and especially so if the agency uses strategies like variable speed limits. When a road is closed (either due to agency action or a crash), the service level is null. Performance Measures and Applicability to Agencies Type of Jurisdiction Type of Operations Modes Covered Urban Rural Mix Public Contracted Interstates Arterials Mix Transit Percent of time road segments meet agency - defined level-of-service thresholds during winter storms High Low. Unlikely to have LOS thresholds during event. Low High High High Medium. More difficult to establish and report service levels for arterials. Medium High Percent of segments meeting time to regain or recover to acceptable criteria for agency-defined segments after the end of event High High High High High High High High High Percent of trips within accepted difference between measured travel time index and additional expected travel time index for snow and ice events for selected routes High Medium Medium High Low. The measure is trip-based, and different segments have different contract operations. High Medium Medium High Five-year rolling average of number of fatalities and injuries during a winter season â â â High. Trends provide valuable information about agency priorities. Low. Not controllable but still might be a valuable indicator. â â â â Customer satisfaction ratings for snow and ice response â â â High High â â â â Cost of snow and ice control to meet established performance criteria for a given winter severity â â â High High â â â â Agency within acceptable difference between expected and actual use of salt and other materials in a season â â â High High â â â â Table 10. Efficacy of measures in supporting agency and contractor decision making.
Development of Performance Measures 53 The selected service-level threshold is defined by the agency and will depend on one or more of the following functions: â¢ Roadway functional class. Higher levels of service may be expected for higher-priority road- ways (e.g., Interstates). â¢ Observed severity of the event. Lower levels of service may be acceptable for more severe events. â¢ Day of week. Lower levels of service may be acceptable for certain days (e.g., Saturdays, Sundays, holidays). â¢ Time of day. Lower levels of service may be acceptable during certain periods (e.g., midnightâ 6:00 a.m.). For the performance-level thresholds, agencies should select an event duration that consistently encompasses most winter weather events. Different agencies will have different durations that they consider a winter weather event (e.g., from first to last inch of accumulation or a combination of weather indicators). As an example, an agency may establish the following criteria: â¢ Interstate highways should have no more than 1 in. of winter precipitation accumulation 100% of the time during a winter weather event with 1 in. or less accumulation per hour. â¢ Interstate highways should have no more than 1 in. of winter precipitation accumulation 75% of the time during a winter weather event with more than 1 in. of accumulation per hour. â¢ Other highways should have no more than 1 in. of winter precipitation accumulation at least 80% of the time during a winter weather event with 1 in. or less accumulation per hour. â¢ Other highways should have no more than 2 in. of winter precipitation accumulation at least 50% of the time during a winter weather event with more than 1 in. of accumulation per hour. â¢ Secondary roadways are not subject to this performance measure. 126.96.36.199 Weaknesses and Limitations of the Measure â¢ Conditions that are considered acceptable may differ between urban and rural areas or by road type. â¢ The accuracy of the measure depends on the ability to report service levels consistently during an event, which could differ significantly between urban and rural areas or by road type. â¢ There may be subjectivity in estimating service levels based on field reports since different reporters may view the road conditions differently. Use of a structured process can mitigate some of the subjectivity, but not all. â¢ This measure is not appropriate for agencies that routinely use closures as a part of their winter maintenance strategies, particularly in remote or mountainous areas. â¢ Anticipated conditions may differ from actual storm conditions, which could lead to an under- or overestimation of the measure. 188.8.131.52 Variations in the Measure â¢ Agencies could measure a variety of service-level thresholds: â Accumulation-related threshold. Accumulated snow and ice between passes by snow- plows; could be estimated by plow-driver observations or measured by RWIS; blowing and drifting snow could affect the accuracy of these measurements. â Friction-related threshold. Measured grip factor reported by RWIS or plow trucks. â Travel speedârelated threshold. Measured travel speeds, which could have wide variations given lower traffic volumes during major weather events, particularly overnight; measure
54 Performance Measures in Snow and Ice Control Operations could also encourage higher speeds when they are not warranted, particularly where variable speed limit systems are in place. â Access-related threshold. The capacity of available infrastructure (e.g., per lane opened). This could be an alternative to speed-related thresholds because they do not encourage the use of higher speeds. â¢ Measures that rely on observations may be subject to increased subjectivity and have fewer data points to accurately calculate the time that a roadway segment did not meet the service- level threshold. â¢ Training for individuals who report service-level threshold observations increases consistency in reporting. A structured visual inspection technique may be used to minimize subjectivity. â¢ Expanding the pool of reporters to include citizens, law enforcement, and other personnel increases reporting frequency for certain service-level thresholds. â¢ Equipping vehicles with AVL for enhanced reporting may increase frequency of reports on certain service-level thresholds. â¢ Leveraging mobile and RWIS data will increase accuracy for certain service-level thresholds. â¢ The service-level threshold criteria are likely to vary by agency by roadway functional class, severity of the event, day of week, and time of day. â¢ Service-level thresholds can be reported at any scale (e.g., segment, district, state). 184.108.40.206 Data Elements Required for the Measure â¢ Performance target(s) for service-level threshold. May vary based on road segment pri- oritization, hour of day, day of week, storm severity, and the respective definition for the service-level threshold. â¢ Time stamp for beginning and end of winter event. Times at which winter precipitation started and stopped for a given road segment; may be logged from nearby RWIS snow accumulation data, observed weather radar, or agency personnel reports in the area. â¢ Time stamp for observed service-level threshold. Time that the pavement status was observed (may be different from the time stamp of the submitted report). â¢ Service-level threshold. Calculated for each segment based on available reports and time for observed service-level threshold. â¢ Road segment information. The road segments identified as a priority for calculating this measure; if service-level criteria vary within an agency by road type or area, these designations are for each road segment. Average travel speeds will be needed to calculate speed-related thresholds. â¢ Weather information. Data from RWIS regarding rate or total precipitation or grip are needed to determine storm severity, an accumulation-related threshold, or a friction-related threshold. â¢ Posted speed limit information. Information regarding posted speed limits should be used if agency changes speed limits in response to weather. â¢ Road closure information. Information regarding the mileage and duration of road closures is needed to calculate a closure-related threshold. 4.3.2 Percent of Segments Meeting Time to Regain or Recover to Acceptable Criteria for Agency-Defined Segments After the End of Event A key objective of maintenance operations is to reach acceptable pavement conditions. This measure is used to assess the performance of winter storm management and response for an event (i.e., by reporting expected time to regain acceptable pavement conditions). This measure supports event response decisions and post-event analysis of labor and material usage.
Development of Performance Measures 55 Translating time-stamped surface conditions to the winter weather event status allows an agency to determine how quickly the segment recovered from the event. 220.127.116.11 Measure Definition This performance measure assesses the amount of time that passes from the end of a winter event until an acceptable surface condition once again exists (e.g., bare pavement). This measure need not apply to all segments with the same criteria. The following criteria are defined by the agency (and may vary for different roadway segments): â¢ Acceptable condition. Defined by the percentage of the affected segment that must be at acceptable criteria to meet target. â¢ Performance target. Desired time to reach a specific pavement condition, which may vary by roadway segment priority. Pavement conditions may be defined using agency guidelines. â¢ Roadway segment prioritization. Roadway segments for which to track this measure, and relative importance of selected segments for reaching desired pavement condition. â¢ Storm severity. Given high sensitivity to storm severity, agencies should normalize this measure by developing the measure for different intensity of storm events taking into account the nature of the storm. Included in the definition is event duration, which agencies should select to consistently encompass most winter weather events. Different agencies may have different durations that are considered (e.g., from first to last inch of accumulation or a combination of weather indicators). 18.104.22.168 Weaknesses and Limitations of the Measure â¢ Conditions that are considered acceptable may differ between urban and rural areas or by road type. â¢ The accuracy of the measure will depend on the frequency of checks made, which could differ significantly between urban and rural areas or by road type. â¢ Subjectivity is a concern since different reporters may view the road conditions differently. Use of a structured process like PSIC can mitigate some of the subjectivity. In general, field personnel such as snowplow drivers are required to report conditions. â¢ Anticipated conditions may differ from actual storm conditions, which could lead to an under- or overestimation of the measure. 22.214.171.124 Variations in the Measure â¢ This measure is typically observed for specific locations and roadway segments driven by plow operators. â¢ Training for individuals who report pavement surface condition status increases consistency in reporting. â¢ Expanding the pool of reporters to include citizens, law enforcement, and other personnel increases reporting frequency for pavement surface condition status. â¢ Equipping vehicles with AVL for enhanced reporting may increase frequency of reports on pavement surface condition status. â¢ Leveraging mobile and RWIS data will increase accuracy of pavement surface condition status. â¢ The pavement condition criteria are likely to vary by agency. For example: â The percentage of the affected pavement segments that is bare (e.g., 95%) may differ. â Pavement condition criteria for urban and rural areas, as well as by road type, may differ. â A structured visual inspection technique like the PSIC (Blackburn et al. 2004) may be used to minimize subjectivity. â¢ Can be reported at any scale (e.g., segment, district, state).
56 Performance Measures in Snow and Ice Control Operations â¢ A variation of this measure substitutes a time to return to normal speed or free-flow speed in lieu of bare pavement. While the variation may benefit from less subjectivity and, potentially, more abundant and precise time stamps offered by speed data, this measureâs variation could encourage unsafe driving speeds instead of improved performance of snow maintenance activities. 126.96.36.199 Data Elements Required for the Measure â¢ Road segment information. The road segments identified as a priority for calculating this measure; if pavement criteria vary within an agency by road type or area, these designations need to be known for each road segment. â¢ Time stamp for end of winter event. Estimated time when winter precipitation stopped for a given road segment; may be logged from nearby RWIS snow accumulation data, observed weather radar, or agency personnel reports in the area. â¢ Pavement surface condition status. Percentage of a road segment that is at the predefined acceptable criteria; reported by agency personnel in the field and citizen reporters. â¢ Time stamp for observed pavement surface condition status. Time at which the pavement status was observed. (This is different from the time stamp of the submitted report because drivers may not submit a report until arriving at their destination.) 4.3.3 Percent of Trips Within Accepted Difference Between Measured Travel Time Index and Additional Expected Travel Time Index for Snow and Ice Events for Selected Routes Agencies seek to provide reliable service through dependable travel times, as measured from day to day or across different times of the day. In this sense, consistency in travel time is an important measure of service quality and mobility for travelers since there is real value in understanding how congestion and service behave throughout the operation of a transportation system. 188.8.131.52 Measure Definition The travel time index (TTI) is defined as the ratio of the peak-period travel time to the free-flow travel time, with averages across urban areas, road sections, and periods being weighted by VMT. This measure of performance looks at the difference between measured TTI during storms versus prespecified additional TTI for key trips defined by an agency (i.e., specific originâdestination pairs) and for specific storm severity (or a range thereof). For example, if under normal conditions the TTI for a trip is 1.5 (i.e., it takes 1.5 times free- flow time), then the agency can establish an additional 33% increase in the index due to snow and ice for a medium-severity event, making the TTI equal to 2 for the specified condition. Then, the agency estimates the percentage of the event duration during which the TTI threshold was met (i.e., TTI is less than 2). Additional TTI impacts for an event can be estimated based on expert judgment, calculated based on historical data, or modeled. The selected additional TTI is defined by the agency and will vary depending on the following: â¢ Roadway functional class. Lower TTI may be expected for higher-priority roadways (e.g., Interstates). â¢ Observed severity of the event. High additional TTI may be acceptable for more severe events. â¢ Day of week. Higher TTI may be acceptable for certain days (e.g., Saturdays, Sundays, holidays). â¢ Time of day. Higher TTI may be acceptable during certain periods (e.g., midnightâ6:00 a.m.).
Development of Performance Measures 57 Implied in the Definition â¢ Event duration. Agencies should select a period that consistently encompasses most winter weather events. Different agencies will have different definitions for the durations that are con- sidered a winter weather event (e.g., from first to last inch of accumulation or a combination of weather indicators). â¢ Trips. Not all roads and segments or periods will be monitored under this measure. Agencies would have to identify key trips that are of importance to stakeholders. These may include: â Priority originâdestination pairs along key routes, â Emergency routes, and â Time periods. 184.108.40.206 Weaknesses and Limitations of the Measure â¢ Agencies must be capable of monitoring and recording travel time for the duration of an event. The additional TTI for each defined storm severity can be estimated using historical data. â¢ This measure will only be calculated for key trips and not for all segments. The agency will identify a subset of trips for which this measure will be calculated. â¢ This measure looks at trips that may encompass many types of roadways, agencies, and jurisdictions. â¢ The additional TTI criteria are likely to vary by agency and jurisdiction. â¢ The accuracy of this measure will vary depending on the ability to monitor travel time consistently during the event, which could differ significantly between urban and rural areas or by road type. â¢ This measure is not appropriate for agencies that routinely use closures (or if the roads were closed due to crashes) as a part of winter maintenance strategies, particularly in remote or mountainous areas. â¢ The progression of events is a critical factor because anticipated conditions may differ from actual storm conditions, which could lead to an under- or overestimation of the measure and toward bias in the final result. 220.127.116.11 Variations in the Measure â¢ Agencies could also measure other reliability indicators, such as: â BI. Buffer time is the extra time required to make a trip, defined as a percentage of the average (i.e., calculated as the 95th percentile minus the average, divided by the average). â PTI. Planning time is the total travel time and includes buffer time. The index is measured as the ratio of the 95th percentile travel time to the free-flow travel time. 18.104.22.168 Data Elements Required for the Measure â¢ Historical travel time information. These data are needed to define the criteria for the addi- tional TTI. This information needs to be correlated with or normalized by its respective storm severity. â¢ Current travel information. This information is needed to estimate the TTI of selected trips in (near) real-time or at a defined frequency within the duration of the storm (e.g., every 30 min). â¢ Time stamp for beginning and end of winter event. Estimated time that winter precipita- tion started and stopped for a given road segment; may be logged from nearby RWIS snow accumulation data, observed weather radar, or agency personnel reports in the area. â¢ Road segment information. The road segments identified as a priority if service-level criteria vary within an agency by road type or area. Average travel speeds are needed to calculate a travel speedârelated threshold.
58 Performance Measures in Snow and Ice Control Operations â¢ Weather information. Data from RWIS regarding rate or total precipitation or grip will be needed to determine storm severity, an accumulation-related threshold, or a friction-related threshold. 4.3.4 Five-Year Rolling Average of Number of Fatalities and Injuries During a Winter Season This measure allows for seasonal evaluations and can be an important input to both main- tenance and incident management planning. It may be used to identify priority locations and other locations in need of specific safety interventions, technologies, programs, practices, or enforcement. 22.214.171.124 Measure Definition This performance measure indicates the number of fatal and serious injury crashes as related to the winter season or winter weather events. Serious injuries are defined by the FHWA following the Model Minimum Uniform Crash Criteria âSuspected Serious Injury (A)â attribute found in the âinjury statusâ data element (Governors Highway Safety Administration and U.S. DOT 2012). The measure is calculated every winter season and averaged to account for seasonal differences. Multiple seasons should be used to calculate an average to normalize for expected variations in crash rates. The exact measure of fatal and serious injury crashes reported by an agency may vary based on the availability of resources and data, including: â¢ Detailed crash records. Reliably documented weather-related causal factors, as well as time of the crash, specific location, and the number of persons involved, which can be easily queried and examined, facilitate calculation of a more comprehensive measure. â¢ Accurate traffic volume data. Quality volume counts and estimates during winter weather events or the season to generate a reliable value of VMT could facilitate calculation of fatality rates. â¢ Weather event data. Winter weather event start and end times are required to identify crashes occurring during the event. An agency may select one or more fatality- and injury-related measures based on the following considerations and data availability: â¢ Fatal crashes versus fatalities. Crashes involving a fatality versus total number of individuals killed in crashes. â¢ Serious injury crashes versus serious injuries. Crashes involving a serious injury at worst versus total number of individuals with a serious injury (including those with a serious injury in a crash that also had a fatality). â¢ Season versus winter events. Include versus exclude crashes that do not occur during a winter weather event. â¢ Rate versus number. Normalizing versus not normalizing for estimated VMT. Included in the Definition â¢ Season duration. Agencies should select a period that consistently encompasses most winter weather events. Different agencies will have different definitions for the durations of what is considered winter (e.g., from first to last storm, based on frequency of events, based on temperature or a combination of weather indicators, or simply by calendar dates). â¢ Number of seasons. Given expected variations in crash rates, a rolling average of multiple seasons should be used to manage regression to the mean and better indicate long-term trends; a 5-year average is consistent with the Safety Performance Management Measures Final Rule (FHWA 2017b).
Development of Performance Measures 59 126.96.36.199 Weaknesses and Limitations of the Measure â¢ Availability of crash data is often delayed due to processing. In part, this is because some crashes cannot be immediately categorized since generally any injury that results in a death within 30 days is listed as a fatality. â¢ The accuracy of fatality and injury rate measures will vary by the availability of actual traffic volume data and reliable estimates of traffic volumes during winter weather events, which could be particularly challenging for secondary and rural roadways. â¢ Identifying individuals sustaining serious injuries in fatal crashes may complicate the ability to identify the total number of serious injuries versus number of serious injury crashes. â¢ Collecting detailed reliable information on the factors that led to the incident, including the condition of the road at the time of the incident, is challenging. â¢ Attributing crashes to winter maintenance activities is difficult since there may be multiple causal factors not related to snow and ice controlâhence the need for detailed crash reports. â¢ Relationships with safety management groups within the DOT are needed to obtain dis- aggregated crash data. â¢ This measure does not take into account crashes involving less severe injuries or no injuries. 188.8.131.52 Variations in the Measure This measure can be reported in ways that may be more helpful and relevant to winter maintenance operations given the availability of resources and quality data: â¢ Winter season fatal or serious injury crashes. The number of all fatal or serious injury crashes over the designated winter season period. This level of aggregation works where snow and ice events are common. â¢ Fatal or serious injury crashes during winter weather events. The number of fatal or serious injury crashes occurring during winter weather events, given crash and winter weather event start and end times. This excludes dates and times when no winter weather condition was present. This might be particularly useful where snow and ice events are sporadic. â¢ Winter season fatal or serious injury crash rate. The number of winter season fatal or serious injury crashes divided by the estimated VMT occurring during the winter season, given availability of reliable traffic volume estimates. â¢ Fatal crash rate during winter weather events. The number of fatal crashes occurring during winter weather events divided by estimated VMT occurring during those events, given crash and winter weather event start and end times and ability to reliably estimate traffic volumes during weather events. â¢ Measures using fatalities and serious injuries. All of the previous measures could be calculated using the number of individuals killed or injured instead of the number of respective crashes, which may be preferred given consistency with the Safety Performance Management Measures Final Rule (FHWA 2017b) but is potentially more difficult to calculate from available data. â¢ Measures using serious injuries. All of the previous measures could be calculated using the number of individuals seriously injured instead of the number of serious injury crashes, which may be preferred given consistency with the Safety Performance Management Measures Final Rule (FHWA 2017b) but is potentially more difficult to calculate from available data. â¢ Measures by road type. All of the previous measures could be calculated at any scale by road type and area (e.g., segment, segment type, district, state). In addition, agencies need to consider: â¢ The quality of available crash and traffic volume data as well as the accuracy of defined start and end times of winter weather events will determine the ability to isolate the fatalities and injuries occurring during winter weather events and estimate the VMT during the events, which will affect the accuracy of the calculated measure.
60 Performance Measures in Snow and Ice Control Operations â¢ If reliably documented causal factors are available in detailed crash records, these may be examined to identify specific areas to improve winter maintenance operations. However, inconsistencies and subjectivity with reporting causal factors make this less conducive for calculating a performance measure. â¢ Given transportation management center (TMC) coverage of the entire area of interest, incident data may be easier to obtain than crash records; however, TMC incident logs are likely to have less detail. â¢ Rolling 5-year averages are appropriate to manage regression to the mean, provide a better picture of long-term trends over time, and be consistent with the Safety Performance Manage- ment Measures Final Rule (FHWA 2017b). â¢ Some agencies use different reporting scales for injury crashes and may need to adjust the calculation of this measure. â¢ Agencies may calculate additional or similar measures to include all types of injuries or injury crashes during weather events instead of only serious injuries. 184.108.40.206 Data Elements Required for the Measure â¢ Records of fatal crashes. At a minimum, the number of fatal crashes occurring during each winter season month is needed. More detailed information, such as causal factors, location, time, and number of individuals involved in the crash, can help improve the measure. How- ever, TMC incident records may not log all crashes or their severity, and official crash records have a time lag associated with them. â¢ Injury crash records. At a minimum, the number of serious injury crashes occurring during each winter season month is needed. More detailed information, such as causal factors, location, time, and number of individuals seriously injured in fatal and serious injury crashes, can help improve the measure. However, TMC incident records may not log all crashes or their severity. 220.127.116.11 Optional Data â¢ Traffic volume. To calculate a rate of fatalities or fatal crashes, actual volumes from traffic count stations are preferred, but a volume estimate, adjusted if necessary to account for lower than normal traffic during winter weather conditions, will suffice. â¢ Time stamp for beginning and end of winter event. To calculate the number or rate of fatalities or fatal crashes occurring during winter weather events, the estimated time at which winter precipitation began and ended for a given road segment is needed; these times may be logged from nearby RWIS snow accumulation data, observed weather radar, or agency personnel reports in the area. 4.3.5 Customer Satisfaction Ratings for Snow and Ice Response Although the results can be subjective and expensive to obtain (through comprehensive and representative surveys), direct customer feedback can be helpful in ensuring support for snow and ice control operations. This measure provides support to event response decisions and post-event analysis and helps an agency see its customersâ perception of performance changes. 18.104.22.168 Measure Definition This performance measure analyzes the satisfaction of transportation system users by tracking traveler feedback at a regional or statewide level. Traveler satisfaction and demographic data are gathered through periodic surveys, focus groups, or other approaches. It is likely that traveler satisfaction will decrease during more severe events, so severity can be considered in evaluating
Development of Performance Measures 61 performance. Collecting information about demographics enables agencies to consider whether satisfaction varies based on socioeconomic status. Data for the measure can be collected in two ways: â¢ Seasonal survey of customer satisfaction. A survey is conducted once a season to gather data on travelersâ perceptions of the snow and ice response. While conducting a survey is simple, the inability of travelers to recall their satisfaction during the entire snow and ice season may skew responses toward more recent events. â¢ Survey after specific events. A survey is conducted after specific events through existing public-facing interfaces. However, such surveys might be biased since obtaining a statistically sound sample is unlikely. Included in the Definition â¢ Season duration. Agencies should select a period that consistently encompasses most winter weather events. Different agencies will have different definitions for durations that are considered winter (e.g., from first to last storm, based on frequency of events, based on temperature or a combination of weather indicators, or simply by calendar dates). â¢ Event duration. Agencies should select a period that consistently encompasses most winter weather event durations. Different agencies will have different definitions for durations that are considered a winter event (e.g., from first to last inch of accumulation or a combination of weather indicators). The Missouri Department of Transportation (MoDOT) uses road rallies, customer surveys, and report cards to monitor the degree to which the public accepts the agencyâs performance. MoDOT hosts a gathering of citizens to drive around while accompanied by a moderator who tracks their comments as they assess the ride (i.e., road) quality. MoDOT spends approximately $200,000 each year on its public phone survey and a survey of the media and other partners (e.g., public officials and organizations such as the Association of General Contractors). Customer relations personnel generally design the survey mechanism with input from the department on the agency-wide focus areas (Yurek et al. 2012). 22.214.171.124 Strengths, Weaknesses, and Limitations of the Measure â¢ Ideally, surveys would be conducted soon after the event has ended and for all events. How- ever, this might represent a significant cost, depending on the desired number of responses (or response rate) and the applied survey methodology. Each sampling methodology could introduce different biases into the responses. â¢ The quality of this measure is highly dependent on the quality of the information collected, which could present limitations for analyzing and reporting survey data. â¢ The quality of this measure improves with consistent and representative observations over time and a higher response rate. Although initial assessments may be not usable, consistency in survey questions and methodology will allow for longer-term use as a performance measure. â¢ The flow of events is an important factor because anticipated conditions may differ from actual conditions, which could lead to an under- or overestimation of the measure and toward bias in the final result. 126.96.36.199 Data Elements Required for the Measure â¢ Demographic information. Socioeconomic data that can be used to characterize households. â¢ Storm/season information. Data and times of all storm/season parameters (e.g., accumulation) for the surveyed areas.
62 Performance Measures in Snow and Ice Control Operations 4.3.6 Cost of Snow and Ice Control to Meet Established Performance Criteria for a Given Winter Severity Agencies attempt to correctly translate their usage of different resources into the cost of winter maintenance operations and use this information to assess the efficiency of their spending. âCostâ here refers to a standardized cost, which can be the cost per number of lane miles under the agencyâs jurisdiction, for instance. 188.8.131.52 Measure Definition This performance measure is a highly visible parameter of local and state expenditures. The main challenge is to develop a monetization approach that captures the complexity that exists in most state and local agencies. In addition, agencies might have limited ability to control the costs. For example, the cost of materials may be determined by factors outside the jurisdictionâs control. Also, fuel prices fluctuate for reasons unrelated to winter maintenance activities, and personnel have multiple duties and only spend a portion of their jobs on snow and ice control, making assigning the personnel cost a challenge. Winter maintenance cost information can be viewed as the output of combining usage indicators of labor, equipment, materials, and other resources with respective cost information. Thus, this measure tracks the true cost of winter operations per storm and season. In order to estimate the efficiency of spending, the overall cost needs to be standardized by one or more characteristics of the maintained/served area and the severity of the event or season. There are many factors that drive cost that should be considered when estimating cost and effi- ciency of spending, including: â¢ Geographic size, â¢ Functional class or roadway and priority segments, â¢ Density of roadways (i.e., length of roadways per area), â¢ Rural versus urban, â¢ Microclimates and hot spots, â¢ Timing of events, â¢ Number of events, and â¢ Intensity of events. 184.108.40.206 Weaknesses and Limitations of the Measure â¢ Overall cost is mainly defined by labor, materials, and fuel. While these are commonly tracked variables, they tend to vary in capability and accuracy. â¢ Cost efficiency may not always be transferable to other locations. For instance, two agencies/ regions can have the same LOS objective but significantly different costs due to the nature of the area. However, this measure can be compared with itself over time. â¢ Determining the cost of winter maintenance may be easier for contracted operations where there is a well-defined contract value. â¢ Detailed information on (unit) cost and weather data by event is needed to estimate this mea- sure; the agenciesâ records are the main source of information. â¢ The flow of events is an important factor since anticipated conditions may differ from actual conditions, which could lead to an under- or overestimation of the measure and toward bias in the final result. 220.127.116.11 Variations in the Measure â¢ Depending on their capabilities, agencies can standardize their costs through more general or detailed factors, such as:
Development of Performance Measures 63 â Miles covered, â More detailed geographical areas, such as census tracks, â Cost per type of activity (e.g., anti-icing and deicing), and â Traffic level [winter average daily traffic (WADT)] that is serviced. 18.104.22.168 Data Elements Required for the Measure â¢ Unit cost. Cost of each unit of every resource used in winter maintenance activities. â¢ Resource usage. Total amount of resources used, detailed by category. At a minimum, detailed information on amount of material, staff, equipment, and fuel used during each storm is needed. â¢ Weather information. Data from RWIS regarding rate or total precipitation or grip will be needed to determine storm severity, an accumulation-related threshold, or a friction-related threshold. â¢ Time of beginning and end of maintenance activities. The beginning and end times of all winter maintenance activities are needed as a proxy for the usage of some resources or to estimate the efficiency of the activities. â¢ Mileage and functional classes of roadway. Total length of the maintained roads and functional classes. â¢ LOS and recovery threshold. 4.3.7 Agency Within Acceptable Difference Between Expected and Actual Use of Salt and Other Materials in a Season An important objective of agencies is to achieve a specific LOS, providing a safe and reliable road network while using only the materials necessary to maintain a sustainable winter main- tenance operation. This performance measure is used to assess the amount of materials used in a given season to achieve this objective. Material usage, when consistent with the goals of the agency and the severity of the conditions, will provide winter maintenance managers an overall perspective of how the agency is performing. 22.214.171.124 Measure Definition This performance measure assesses the amount of material an agency uses in a given winter season for highway maintenance. The amount of material an agency uses is affected by multiple factors. To use material usage as a performance measure, these factors must be taken into consideration so that consistency from storm to storm or season to season can be achieved. Agencies often review material usage as an indicator rather than a performance measure. Material usage alone is not an effective performance measure. Varying factors or practices from storm to storm or season to season will result in inconsistencies. Factors that may influence material usage include storm/season severity; varying levels of service; proactive approaches like treated salt, anti-icing, and pre-wetting; computerized dispensing equipment; maintenance decision support systems; calibration of equipment; and yearly weather patterns. To the extent possible, these factors should be considered when normalizing the usage of salt and other materials. As such, it is important to model an expected amount of salt and material usage based on winter severity and the objectives of the response. This requires historical data to correlate actual material expenditures to measured severity, miles of roadway by functional class, and LOS parameters for particular regions. The performance measure then is an acceptable difference (plus or minus 10% for example) between the expected and actual material usage.
64 Performance Measures in Snow and Ice Control Operations Included in the Definition â¢ Season duration. Agencies should select a period that consistently encompasses most winter weather events. Different agencies will have defined different durations as winter (e.g., from first to last storm, based on frequency of events, based on temperature or a combination of weather indicators, or simply by calendar dates). â¢ Expected amount of material use. A modeled estimate of material use based on winter severity, LOS, and historical data. 126.96.36.199 Weaknesses and Limitations of the Measure â¢ Direct assessment of the environmental impact of winter maintenance practices is difficult, making material use a good proxy. â¢ Techniques and quality of materials have a direct impact on the amount of material used to achieve the stated operational objectives. Operational approaches such as pretreatment can influence the final amount of material used. â¢ Advanced capabilities require in-vehicle monitoring systems that report, in near real-time, material usage and minimize the need for manual data collection. â¢ This measure relies on development and acceptance of a model for an expected amount of material use. However, anticipated conditions may differ from actual conditions, which could lead to an under- or overestimation of the measure. 188.8.131.52 Variations in the Measure â¢ Depending on their capabilities, agencies could also standardize their use of materials through more general or detailed factors, such as: â Lane miles covered, â More detailed geographical areas, such as census tracks, and â Traffic level (WADT) that is serviced. 184.108.40.206 Data Elements Required for the Measure â¢ LOS. An agency needs to determine an LOS for its road system. LOS may vary for different road segments based on agency priorities and public use. Regardless of the LOS requirements (bare pavement, wheel tracks, or snow covered), the same LOS must be targeted consistently in order to consider material use as a performance measure. LOS may be measured in order to ensure that under- or over achievement is not occurring. â¢ Material used. An agency needs to have a consistent and accurate way of collecting the data for the amount of materials used (solids and liquids). This may be achieved by capturing the amount or weighing the material as it is loaded, by using onboard data to collect the amount of material dispensed, or by operators keeping good records of the material being loaded and used and returned at the end of an event. These data may be compiled per event and per season and should include materials used in pre-storm operations and in-storm operations in order to capture all the material being used. â¢ Treatment recommendations and calibration. Accountability may be the biggest factor in material usage as a performance measure. Consistency in material being dispensed by trucks and operators following recommendations is needed to ensure that material usage is uniform in every condition and every event. Equipment must be calibrated prior to a season and every time material changes or something with the equipment changes (like a breakdown or broken hose). Material usage must be consistent from operator to operator given similar conditions. â¢ Model techniques and results. This measure depends on the expected amount of salt and material usage based on winter severity and the objectives of the response. It requires historical data to correlate actual material expenditures to measured severity, miles of roadway by func- tional class, and LOS parameters for particular regions.
Development of Performance Measures 65 4.4 Target Setting 4.4.1 Background on Target Setting This section summarizes available guidance on target setting for performance measurement. Performance target setting is an important and complex process through which agencies analyze expected performance and account for factors that will affect performance in the future, including levels of available funding and the relative emphasis placed on achieving different targets for the transportation system. Specifically, some of the factors that must be considered are: â¢ Financial resources. A realistic projection should be made of what could be accomplished with available funding levels. â¢ Technical considerations. Targets should be achievable based on current and forecasted conditions and trends and accounting for external factors that could affect performance levels. â¢ Policy considerations. Targets should reflect existing priorities and policies and be based on public involvement, customer feedback, or legislative or executive direction. â¢ Economic factors. Target setting should take into consideration how to maximize benefits in relation to investments or achieve the highest return on investment. â¢ Correlation between targets. Potential correlations between targets that may influence their achievement should be highlighted (i.e., improving time to bare pavement while reducing the amount of salt and fuel used). As agencies go through multiple cycles of monitoring and evaluating performance, their ability to develop realistic targets and measure the outcomes that result from target achievement improves. Throughout the transportation field, target setting generally remains part science and part art, informed by both qualitative and quantitative data and information. Time frames are another important consideration in setting targets. Short-, mid-, and long-range targets are all useful, and having a combination of all three to measure progress toward achieving longer-term goals could be useful. The following are a few different types of target-setting methods. Rarely does a target-setting process fit neatly into one of these categories, but all are ways through which the process can be informed. â¢ Policy-driven methods (established by executive management or a legislative body, which might arise because of public discontent with transportation issues). â¢ Analysis-driven methods (based on modeling or other tools that provide information about expected levels of performance). â¢ Consensus-based methods (established through a collaborative planning process with input from a variety of stakeholders). â¢ Customer feedbackâbased methods (direct feedback from customers through surveys and outreach is used to help define targets). â¢ Benchmark-based methods (through comparisons with peer agencies). For snow and ice control, establishing targets likely will involve some combination of these approaches, particularly policy priorities, analysis, consensus, and customer feedback. Adams et al. (2014) identified methodologies for selecting target levels of service for maintaining and operating highway assets to improve agenciesâ performance with respect to managing highway assets. Cambridge Systematics et al. (2010) provided additional information about methods that managers of state DOTs and other agencies can use for setting performance targets to achieve multiple objectives, interact with multiple decision makers and stakeholder groups, and use data management systems to support performance-based decision making. The steps for setting targets provided in this report (see Figure 10) build on the National Highway Institute workshop Steps to Effective Target Setting for Transportation Performance Management (National Highway Institute 2016). The following subsections describe each step in more detail.
66 Performance Measures in Snow and Ice Control Operations Step 1 â Define Purpose Agencies should be able to link their targets directly to their goals and need to clearly state their goals so that they can be converted into measurable objectives. Therefore, agencies need to: â¢ Assess why they are setting the snow and ice control operation targets; â¢ Link performance to the agencyâs overall and winter maintenance objectives, resources, and requirements; and â¢ Use the agencyâs overall and winter maintenance purposes to inform target parameters and guide target establishment. Step 2 â Set Target Parameters Three important elements need to be defined for each target: â¢ Target portrayal. Method for portraying the change in performance [e.g., percent change, number, rolling average, return to x-year value/level, and directional (up/down)]. â¢ Time frame. Duration from the baseline that will be the basis for reaching the target (e.g., 1/5/10/20 years). â¢ Scope. Boundaries and filters applied to the performance area to set the extent of the target. Agencies need to determine what the target should focus on [e.g., National Highway System (NHS), non-NHS, urban/rural, mode, and a specific geography]. Step 3 â Assemble Baseline Data and Analyze Trends Agencies need to define the point of reference that will be used to guide the analysis of winter storm/season performance trends. Therefore, agencies need to: â¢ Assemble historical winter measure data from available data sources and fill any data gaps, and â¢ Plot the data to establish a baseline through the development of statistical analysis that can estimate trend lines. Step 4 â Identify and Assess Influencing Factors Agencies need to understand the factors that they can control and the other forces that can change the course of the target outcome, such as the decision-making process, funding and existing conditions, winter staff and equipment resources and priorities, and external factors. Examples of influencing factors are: â¢ Historical performance trends, â¢ Policy directives, â¢ Business culture barriers, â¢ Capital project commitments, Step 1 â Define Purpose Step 2 â Set Target Parameters Step 3 â Assemble Baseline Data and Analyze Trends Step 4 â Identify and Assess Influencing Factors Step 5 â Establish a Target Figure 10. Steps for setting targets.
Development of Performance Measures 67 â¢ Budget and resource constraints, â¢ Senior management directives, â¢ Agency jurisdiction, â¢ Agency goals and priorities, and â¢ Planned operational activities. Other considerations that agencies need to take into account are: â¢ Fiscal limitations and trade-offs, â¢ Constraints and existing commitments, both within agency and to stakeholders, â¢ How factors will change over the time span of a target, â¢ Risk associated with each of the factors, including magnitude and likelihood of risk, and â¢ Documented assumptions involved with building each factor into the target-setting process. Step 5 â Establish a Target This step builds on the sequence of actions to develop a preliminary list of feasible targets from which a final one can be selected. Figure 11 illustrates an example of the end product of this process. It is important to note that targets can be set through different methods, including policy- driven, analysis-driven, consensus-based, customer feedbackâbased, and benchmark-based methods. Therefore, agencies need to provide clear governance, documentation, and rationale supporting the chosen target. This entails: â¢ Determining who makes the final decision to pick the target, â¢ Defining who provides oversight to setting the target, and â¢ Documenting key decisions and providing transparent information to explain rationale around target choice: â Establishing project selection choices that will affect performance and that link to target and decisions, and â Using the tools available to show link between funded projects and their impact on the target. Modified from National Highway Institute (2016). Figure 11. Example of baseline and target setting.
68 Performance Measures in Snow and Ice Control Operations 4.4.2 Evaluating Winter Maintenance Through Performance Curves Winter maintenance performance curves are graphical representations of how an agency operates under various winter conditions. They illustrate the correlation between selected performance measures and indicators of severity. This guidance summarizes the methodology to develop performance curves into three distinct processes: 1. Data collection and review. Through this process, data are compiled and quality checked for outliers. Outlier data points can be described as data that are not representative of the entire population and, therefore, can lead to misleading or incorrect findings. These points can occur as a result of instrument error or the presentation of abnormal conditions (i.e., unusual weather events for the time of year and location, such as snow in Florida in December). It is always recommended to perform basic quality checks of the data before attempting to develop performance curves. This can be accomplished by plotting the data (e.g., X-Y plots, boxplots, and distribution plots) and calculating basic distributional statistics for each variable. Values that are far outside the range of typical values for that variable should be investigated. 2. Data analysis and visualization. This process entails the development of the performance curve itself. For this, the data are summarized into key performance indicators of historical events (i.e., individual storms and seasons) that are relevant to the preparation of performance curves, such as estimates of snow accumulation, wind speed, and temperature. Based on this information, plots can be developed with the selected indicator of performance on one axis and a severity indictor on the other. 3. Sensitivity analysis. Both performance and severity indicators can fluctuate for a correspond- ing value (e.g., an agency may take 2 h to remove 6 in. of snow during one storm and 3 h to remove the same amount during another storm). Therefore, agencies should perform a sensitivity analysis to evaluate the effects of varying parameters on the performance curve. 4.5 Case Studies Two case studies are presented to illustrate how agencies have been using performance measures and how this compares to the suggested performance measures. 4.5.1 Minnesota DOT Case Study The state of Minnesota experiences high amounts of snowfall, potentially accompanied by high winds, with MinneapolisâSt. Paul International Airport logging a 10-year average of 49 in. of snow per year through the 2014â2015 winter season. The Minnesota DOT (MnDOT) maintains over 30,000 lane miles of roadway using a fleet of approximately 800 snowplows driven by more than 1,800 operators. As a result, MnDOT incurs an average annual winter maintenance cost of about $88 million, which is approximately 25% to 33% of its annual maintenance budget. The MnDOT winter maintenance performance measures appropriately reflect the number and severity of winter weather events and maintenance costs. MnDOT has two primary measures to gauge winter maintenance performance: return to bare pavement and public satisfaction. Measures of safety and material usage are also tracked. MnDOT has researched normal condition recovery time as an alternate, more objective mobility measure instead of return to bare pavement; this would focus more on speed than road condition. Given the large MinneapolisâSt. Paul metro area and several medium-sized metro areas, mobility performance measures are appropriate.
Development of Performance Measures 69 The return to bare pavement measure is based on the following definition: âall driving lanes are 95% free of snow and ice between the outer edges of the wheel paths and have less than one inch of accumulation on the center of the roadwayâ (Minnesota DOT 2017) Plow operators report on bare pavement based on their visual observations, which are used to calculate a measure of time to return to bare pavement following a winter weather event. This number is compared with a target value, which varies by road classification. The final reported measure tracks the frequency that MnDOT achieves targets for the entire winter season. The normal condition recovery time measure MnDOT has been researching is based on traffic data. This measure would provide an automated measure for urban freeways using loop detector data on traffic speed, flow, and density to determine when roadways have returned to normal conditions. This approach might be used to derive a similar but more objective indication regarding mobility to the return to bare pavement measure. The measure of public satisfaction is based on a quantitative telephone survey that has been conducted since 2005 (except in 2007). The survey asks customers to evaluate MnDOT perfor- mance in various maintenance areas. Additionally, the number and severity of crashes are tracked by weather condition as well as the cost and quantity of materials used each season; however, these measures are not necessarily linked to targets or performance measures that are used by MnDOT winter maintenance operations. Specifically, although safety-related winter weather performance measures are a must, these are tracked by the Minnesota Department of Public Safety. Environmental performance measures are implied by state restrictions on chemicals and the Minnesota Pollution Control Agencyâs focus on reducing salt usage in winter maintenance (Minnesota Pollution Control Agency 2017). MnDOT uses a WSI to simplify the comparison of year-to-year winter severity. A single relative number is calculated following the winter for each district and statewide, as shown in Table 11, using the following factors: â¢ Dew point/relative humidity; â¢ Wind speed, gusts, direction; â¢ Frost/black ice; â¢ Precipitation type, duration amounts; â¢ Air temperature; â¢ Road temperature; â¢ Cloud cover; â¢ Blowing snow; and â¢ Surface pressure. Source: Minnesota DOT 2016. District 2013â2014 2014â2015 2015â2016 1 158 93 165 2 125 85 103 3 112 69 92 4 129 92 106 Metro 92 66 71 6 134 88 89 7 110 97 107 8 115 92 97 Statewide 128 87 106 Table 11. MnDOT WSI by district for the 2013â2016 winter seasons.
70 Performance Measures in Snow and Ice Control Operations The targets for return to bare pavement vary by road classification: â¢ Super commuter: 0â3 hours. â¢ Urban commuter: 2â5 hours. â¢ Rural commuter: 4â9 hours. â¢ Primary collector: 6â12 hours. â¢ Secondary collector: 9â36 hours. The target for public satisfaction is a customer response of 7.0 or greater to indicate satisfaction. These findings are analyzed with historical data to establish a trend and target level of satisfaction. MnDOT produces an annual winter maintenance report that summarizes maintenance performance and activities. This report gives context for the road mileage that is maintained and the resources used to complete the maintenance. The report also explains the dynamic nature of winter weather and the WSI that is used to normalize it. The report provides summaries of winter severity, material use, and costs from season to season by district and statewide, ending with the final results of how often MnDOT met bare-lane targets. An example of the findings presented in the report from 2015 are shown in Figure 12 (Minnesota DOT 2016). In more severe winters, districts may redirect summer maintenance funds to winter mainte- nance activities. Districts may also use information in the reports to counteract rising fuel and material costs. Additionally, customer satisfaction measures may be used to inform target setting for return to bare pavement. MnDOT achieved statewide snow and ice control targets for the measure for returning to bare pavement in nine of 10 winter seasons through the 2015â2016 winter season. The MnDOT approach to winter maintenance performance reporting is similar to the approach suggested by Source: Minnesota DOT 2016. Figure 12. MnDOT reported frequency of meeting bare-lane targets and maintenance costs by season (in millions of dollars).
Development of Performance Measures 71 this guide for mobility and sustainability measures. Specifically, MnDOT reports the frequency of meeting regain time targets for agency-defined segments over the winter season. Regarding environmental performance measures (a component of sustainability), MnDOT and local transportation agencies work with the Minnesota Pollution Control Agency to manage chloride use near critical watersheds. Critical locations are identified first according to road densities of 18% or greater, as illustrated in Figure 13, and second by areas with drinking Source: Minnesota Pollution Control Agency 2017. Figure 13. Critical locations of pollution in Minnesota.
72 Performance Measures in Snow and Ice Control Operations water supply wells with hard or very hard water. A Winter Maintenance Assessment tool has also been developed by the Minnesota Pollution Control Agency for use by winter maintenance professionals across the state to understand current practices, identify areas of improvement, and track progress. The tool can also be used to compare practices with other entities and learn from them in order to achieve the greatest chloride reductions while providing a high level of service (Minnesota Pollution Control Agency 2017). MnDOT reports public satisfaction and the amount of material used, although no target is set for the latter calculation. MnDOT does report on annual costs associated with winter main- tenance. MnDOT currently does not report safety performance measures for the purposes of winter maintenance operations. The Minnesota Department of Public Safety, Office of Traffic Safety publishes annual crash information that breaks out the number and severity of crashes by the weather condition present, as shown in Table 12 from the 2015 Crash Facts Report (Minnesota Department of Public Safety 2016). The effort to incorporate crash-related safety measures into winter performance measure reporting would thus be minimal. The calculation of a performance measure regarding the percentage of time road segments meet agency-defined LOS thresholds during winter storms would be a more challenging effort, but given MnDOTâs experience with reporting the mobility performance measure service-level threshold for return to bare pavement following a winter event, incorporating a similar measure for safety during winter events may be less challenging for MnDOT than for other agencies. In summary, the MnDOT winter maintenance performance reports contain information for about half of the measures suggested in this guide. Development of crash-related perfor- mance measures would likely be a minor effort given the availability of data from the Minnesota Department of Public Safety; however, the development of measures to track performance during storms would be more challenging. 4.5.2 Idaho Case Study ITD is responsible for maintenance of approximately 5,000 centerline miles of highway over varying terrain that includes mountainous areas of up to 8,000 ft in elevation. To assess how well the winter budget was being spent and what efficiencies in terms of mobility and safety were being realized, ITD needed a uniform approach for measuring winter maintenance performance. Source: Minnesota Department of Public Safety 2016. Table 12. 2015 Minnesota crashes by weather condition.
Development of Performance Measures 73 In 2007, ITD deployed a network of RWIS sites statewide to monitor atmospheric and pavement conditions. These RWIS sites included nonintrusive pavement sensors that remotely measure temperature and surface conditions (e.g., dry, wet, snow, and ice), layer thickness, and the level of grip. The goals of the winter maintenance performance measures are tied directly to ITDâs stra- tegic plan: â¢ Track progress to maintaining safe roads, â¢ Track progress to maintaining mobility, â¢ Promote economic opportunity by minimizing weather impacts on commerce, â¢ Achieve greater uniformity in winter operations statewide, and â¢ Promote a cost-effective winter road maintenance program within available resources. The parameter of grip helped users in Idaho make a connection between the severity of a storm and the impact it had on vehicle mobility. The grip level is provided as a number between 0 and 1. Higher numbers indicate good grip, so a figure of 0.82 would indicate bare pavement, whereas lower numbers, such as 0.1, would indicate the presence of snow or ice and, thus, very slippery conditions. By looking at surface conditions and the measured grip levels, ITD made the following observations: â¢ >0.6: usually dry (or wet) surface. â¢ 0.5 to 0.6: slush or ice forming. â¢ 0.4 to 0.5: snow pack or icy. â¢ 0.3 to 0.4 icy: vehicles may start sliding off. â¢ <0.3 icy: multiple vehicle slide offs possible; mobility greatly affected. ITD developed an SSI in which sensor data (wind speed, surface precipitation layers, and sur- face temperatures) are inserted into a formula to calculate the index value (Jensen et al. 2013). ( ) ( ) ( )= + +SSI WS max WEL max 300 ST min where WS = wind speed (mph), WEL = water equivalent layer (mL), and ST = surface temperature (Â°F). This formula assigns a numeric value to winter storms, where the higher the number, the more severe the storm in terms of winter operations. The index range is generally 10 to 80 for typical storm events, with severe cold and high winds making it as high as 500. The winter performance index then rates the treatment effectiveness to the storm (recovery time to safe grip). To calculate the winter performance index, ITD collects the time required to recover to safe grip and divides this by the SSI to normalize the results from various winter storm events. winter performance index ice-up time (hours) SSI= where ice-up time is when the grip is below 0.6 for at least a 30-min period. The winter performance index value is then compared with a performance scale (typically 0.0 to 0.7, with a goal of 0.5 or less) to identify how successful the road treatment and timing were.
74 Performance Measures in Snow and Ice Control Operations 220.127.116.11 Winter Mobility Index ITD developed a second performance measure, the winter mobility index, to assess how well road treatments perform when temperatures are below freezing. This performance measure can guide both the timing of treatment application and the selection of road treatment material. The winter mobility index rates the percentage of time with wet pavement and below freezing conditions. = Â°Mobility index percentage of time grip is above 0.6 when surface layer is below freezing (32 F) The winter mobility index (0â1.00) is derived using the percentage of time that road conditions did not significantly impede mobility during a storm event (a safe grip value of 0.6 or higher) when precipitation was on the surface with below freezing surface temperatures being observed. By integrating the performance measures and indices into the visualization application, maintenance managers can view the results of maintenance performance almost immediately after each winter storm, rank the storms, and access their efficiency in dealing with them. ITD can rapidly assess how an individual storm event affects the state and understand on a granular level how effective the agencyâs treatments are. This allows it to better deploy finite resources and train staff to improve its responses for future storms. As part of the department dashboard, ITD also reports the percentage of time highways are clear of snow/ice during winter storms, with a target of maintaining at least 73% unimpeded mobility for the winter season. This measure is reported on sections with RWIS stations installed and is based on the amount of time there is no accumulation present during the event. For the previous three winters, ITD has exceeded its target. Translating to the measures in this guide, ITD generates event-based measures and reports both an LOS measure (percentage of time highways are clear of snow/ice) and a recovery measure (the winter performance index). ITD also includes an SSI in its calculations. In its public dash- boards, fatalities are reported per 100 million vehicle miles but are not attributed to weather conditions. Public dashboards currently do not report the cost of winter response.
75 5.1 Conclusions Snow and ice performance measurement does not occur in a vacuum, and it is influenced by trends and crosscutting issues that occur elsewhere in the transportation field. These trends can provide new ways to define and measure performance for snow and ice control. Figure 14 describes the five opportunities that are highlighted in the guidance as approaches to address some of the challenges. It should be noted that any new technology or system will need to comply with local regulations, and as such these should be reviewed before implementing them (e.g., do not ask for people to send text messages while driving since this is illegal). An effective performance measurement program should be tailored to an agencyâs particular needs, taking into account a wide array of circumstances and capabilities. Thus, no two perfor- mance measurement programs are, or should be, identical. The value of performance measures stems from their ability to support effective and timely decision making at multiple levels within an agency. The specific performance measures used by agencies are largely determined by the timing of the decisions. Using performance measures to guide snow and ice planning, investment decisions, strategies, and tactics can provide a clear basis for action. Once decisions are made, the same performance measures can provide assessments of the decisions and enable adjustments. Using performance measures to support decision making is dependent on consistency of the measures. Consistency of measures is necessary to prevent a whiplash effect in decisions, where a rapidly varying measure can create uncertainty in the nature of the decision. Logical times to review the role of performance measures in decision making include during snow and ice strategic planning and budgeting, annual maintenance reviews and meetings, and after-action reviews. 5.2 Suggested Actions Advancing performance measures for snow and ice control may be contingent on a few key capabilities in the region. Focusing on these capabilities may help jump-start the process and ultimately lead to more sophistication in the definition and use of the measures. As agencies seek to create a core set of performance measures in these areas, it is important to note the following: â¢ No individual performance measure is a perfect representation of the complexity of snow and ice response. A more useful approach to performance measurement and reporting would be based on multiple measures that together provide a balanced report card of agency performance. This report and guide identify seven such measures that can provide an agency with a balanced report card. Not all seven measures need to be used, but assessing their suitability is suggested. â¢ Not all performance measures that are important to an agency can be fully controllable by the agencyâs response activity. For performance measures like safety, the linkage from the C H A P T E R 5 Conclusions
76 Performance Measures in Snow and Ice Control Operations snow and ice control activity performed to the measure may be indistinct, but overall trends may still be valuable for the agency to support investments in snow and ice management. â¢ Starting the process of performance measurement is the first and perhaps the most important step. Searching for the perfect performance measure or a sophisticated analytical approach can sometimes seem an undue burden on an agency. However, by focusing on what can be done immediately and in some priority segments of their jurisdictions, agencies often find that there is a path toward continual improvement of the performance measures once they begin the process of measurement. More importantly, many of the identified measures can be tailored for different capabilities and needs of agencies. â¢ Know what data are available and viable for performance management use. The process for selecting a performance measure must include an assessment of the data available to support the measure. Agencies should know what data are needed, where the data are available, and what attributes apply to the data (e.g., timeliness, frequency, accuracy, coverage). â¢ For snow and ice control, some level of subjectivity in performance measurement cannot be avoided. Field personnel are quite often the only source of road condition updates in states with large sections of rural roadways. In other cases, only a small number of roadways may have the instrumentation required to provide data for performance measurement. With the technological revolution promised by connected vehicles still a few years away, accepting a certain level of subjectivity is necessary. â¢ Clearly understand how performance measures can be used in the decision-making process. Establishing measures, setting targets, and collecting and analyzing data are not trivial tasks. Committing resources to the process should only be undertaken if an agency understands how the results can be used in its decision-making process. â¢ Performance measures identified by agencies need to be simple and easily understood, not only by their stakeholders, but also by their own staff. When creating performance measures, agencies need to be highly aware of the reporting requirements and data needs. The more strin- gent the requirements, the more sophisticated the data need; the less automated the analysis approach, the more challenging the performance measure becomes for agency staff to assess. The following sections summarize suggestions that agencies may consider for the development and deployment of a snow and ice control performance measurement program. Use of real-time road condition reporting systems, maintenance management systems, and the MDSS, and use of real-time environmental data from equipped fleets allow for better and objective reporting of field conditions during and after events. Greater ability to collect real- time maintenance field data Growth in third-partyâprovided probe data on travel speeds enables not only greater situational awareness but also increased roadway coverage for analytics for performance meaures. Growth in probe data availability New partnership models between the public and private sector engage weather, traffic, and maintenance stakeholders to provide a common, shared message of severity, timing, and recommendations. Greater linkages between the transportation and weather community From social media to crowdsourcing to apps, agencies have new ways to not only provide data to travelers but also collect feedback on agency performance. New ways of engaging with the traveling public â¢ â¢ â¢ â¢ â¢ Increased availability of online/cloud-based services allows manipulation of large data sets, including spatial data. New tools for data visualization and performance reporting Figure 14. Opportunities to address challenges in snow and ice control.
Conclusions 77 5.2.1 Define and Use a Weather Event as the Starting Point for Performance Measurement Transitioning from a seasonal approach to management to an event-based, data collection approach allows for greater flexibility in the definition of performance measures. There are many ways of defining an event, but consistency across an agencyâs jurisdiction is more important than the selection of a specific approach. Once an event with a start and end time is defined, data collection, aggregation, and analysis can be tied to specific points in the event timeline (before, during, and after the event). While defining a clear timeline for storms can be challenging, even a subjective determination of the timeline can help organize available data into useful performance categories. 5.2.2 Develop Both a Storm Severity Index and a Seasonal Severity Index The value of performance measurement is greatly enhanced by pairing it with severity at both an event level and a seasonal level. A summary of the methods used by different states to generate a severity measure was provided in Chapter 3. These measures range from simply using a few factors (like intensity of precipitation and amounts) to more complex relationships based on regression models using historical data; the effectiveness of a severity index is simply determined by the degree of correlation with the maintenance response. 5.2.3 Pick Consistent LOS and Recovery Criteria and How They Are Measured Across the Agency A certain amount of subjectivity is unavoidable in snow and ice performance measurement. Nowhere is that subjectivity greater than in defining LOS and recovery criteria. It is important to reiterate that there can be no correct or universally applicable LOS or recovery standard nationally. Rather, they need to be set by agencies to accommodate different regions, roadways, and severity. However, defining how these are measured can be made more consistent within an agency depending on existing capabilities. More importantly, an agency can choose whether it wants to specify an LOS during an event based on the agencyâs objective. 5.2.4 Report Performance Information Telling a story with performance measures is not only possible but essential for snow and ice control programs. Some DOTs post annual performance measurement reports online, and these reports may include a section on winter maintenance activities. Also, some DOTs produce fact sheets and other publications that provide information about winter storm maintenance activities. In general, fewer state DOTs have a dedicated, publicly available winter maintenance performance report. Two DOTs that include external reporting are Minnesota DOT (Minnesota DOT 2016) and Wisconsin DOT (Wisconsin DOT 2014). Several other agencies likely collect and report performance internally. This type of report gathers data on winter weather to assess severity (e.g., precipitation and wind speed) and performance indicators to assess how the agency responded to each event (e.g., material use and cost). These data usually contain temporal and spatial information (i.e., when and where) to help agencies identify good performance as well as areas that need improvement. Through this, they can use their resources more efficiently and achieve greater benefits. Furthermore, reports on performance measures allow regional and county staff to compare resource use with that of their peers.
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