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9 This chapter provides an overview of the literature review findings on various initiatives, strategies, and tools reported in both the published literature and online sources that relate to the scope of the synthesis. The chapter begins with some background on the federal regulations that apply to the administration of the state safety program. Several national studies that have dealt with the topic of roadway safety data in terms of data collection, data access, data integration and maintenance, data management, costs, and implementation are presented in the chapter. Appendix A highlights state DOT efforts at the time this report was written, as found in published articles, conference papers, agency reports, and online sources related to data sharing, integration, and implementation. Background A number of regulations are associated with the various federal aid programs and initiatives that relate to public road safety data. The requirements of MIRE were established through the MAP-21 transportation bill and furthered with continued emphasis in the FAST Act. The HSIP is a federal aid program that funds state safety projects to reduce fatalities on road- ways. All HSIP funding must go toward projects concerning safety, and these projects could include alternatives such as infrastructure improvements, pedestrian crossings, and intersection design (Gigliotti and Scurry 2016). The HSIP funding levels have changed over the years, but, historically, the states have received about $2.3 billion annually for this program. Additionally, under MAP-21, states are required to evaluate and update their safety plans regularly and to upload HSIP annual reports to the FHWA website. The HSIP Final Rule, published in March 2016, contains three major policy changes related to the update cycle for SHSPs, the content and schedule of HSIP reports produced by state DOTs, and MIRE. An effective transportation safety program must take into account the costs and benefits of each data management strategy, encourage more advanced analyses, and support data-driven decision making. FHWA provides state DOTs with a guide for their own sufficient data business plans and helps them plan integrated data management systems. In 2015, President Obama signed the FAST Act, and in response FHWA issued updated guidance in Guidance on State Safety Data Systems (FHWA 2016a) to reflect the FAST Act changes related to data systems under the HSIP. States are required to have a safety data system that includes data from all public roadways, expanded to include crash, roadway, and traffic data; geolocation of safety data to a common highway base map; data analysis and evaluation capabili- ties; and the MIRE subset of required collection. The guidance focused on the state safety data capabilities required and activities related to safety data that are eligible for HSIP funds. C H A P T E R 2 Literature Review
10 Integration of Roadway Safety Data from State and Local Sources Under the analysis and evaluation capabilities, the requirements include establishment of a safety data system that facilitates the performance of safety problem identification and counter- measure analysis. The capabilities must include the capability to identify hazardous locations that are a threat to all motorists. Another requirement relates to establishing an evaluation process for analyzing and assessing the results achieved. Efforts to advance data collection capabilities will complement the NHTSA requirement for states to submit their individual highway safety plans. MAP-21 requires agencies to set performance targets for investments so as to better understand how the investments will affect safety, the condition of assets, and mobility. This requirement gives agencies the incentive to increase their understanding and improve their selection process and includes addressing risks and projects for revenue. Ultimately, it empowers agencies to make a strong case for better advancing a set of projects that best serves the needs of the population. Data Collection The FAST Act and associated FHWA rulemaking require each state to have a safety data system to enforce performance-based goals set forth in the SHSP and HSIP. These goals are measured by improvements made to roadway elements collected in MIRE FDEs. FHWA pro- vided outreach to state, tribal, local, and other agencies and offered technical assistance for implementing safety data integration to support decision making. Its plan is outlined in five preparation steps, as presented in Figure 2. FHWA recommends the collection of suggested roadway and traffic data elements that should be observed, measured, and evaluated to provide accurate data to support safety modifications on roadways. Its FDEs are a subset of 37 MIRE elements that can each be quantified. Data sets containing these elements are used in the analysis of safety measures. Data-based decision making is critical, and support for funding often will not be granted to a project unless it con- tains truly representative data. Therefore, accurate data must first be obtained, then managed and evaluated, and finally shared. FHWAâs report Integrating MIRE: Better Data for Better Decisions to Save Lives (FHWA 2013) lists a series of auxiliary guides and references to aid in establishing a centralized data system. Both current and proposed data collection methods are available in the MIRE Element Collec- tion Mechanisms and Gap Analysis (Mallela et al. 2012) report as well as in the Exploration of the Application of Collective Information to Transportation Data for Safety White Paper (Eccles et al. 2013). These documents list necessary steps to collect data, types of data collection tools, effort and cost, challenges, and lessons learned. MIRE MIS Lead Agency Data Collection (Fiedler et al. 2013) and the MIRE Data Collection Guidebook (Mallela et al. 2013) can help agencies employ certain data collection methods. The MIRE Data Collection Guidebook is one of the prominent products of the MIRE management information system (MIS) effort. It identifies the various issues that states should be cognizant of when collecting information on MIRE elements, such as methods of collecting data and limitations. It also provides data managers and collectors with information about techniques for collecting MIRE data that will allow them to collect the elements more efficiently. The task of integrating and managing data is outlined in the Development of a Structure for a MIRE Management Information System (Altobello et al. 2013), which lists steps taken, resources used, obstacles, and lessons learned. Data managers and engineers need specific performance measures to gauge data quality and to establish data improvement goals. Methods for the analy- sis and advancement of roadway and traffic databases can be found in Performance Measures for Roadway Inventory Data (Council et al. 2013).
Literature Review 11 Through data mining, information that already exists in state-maintained roadway invento- ries and stand-alone information systems maintained for road infrastructure can be accessed. While this method removes the need to manually collect new databases, the currently existing data may not have the necessary information, and not all roadways may be covered. Manual collection guarantees that the information that cannot be found through data mining can be covered; however, the higher labor costs and time required to collect data render it somewhat ineffective. Ground-based imaging through both mobile mapping and lidar integration provides a quicker alternative to manual collection while also generating higher-quality data through both imaging and nonimaging technologies (Mallela et al. 2013). The postprocessing efforts and quality checks are necessary in most cases, but the quality of data and versatility of the method using lidar, regardless of weather conditions, make it effective in the majority of situations. Airborne technologies present a cost-effective and efficient approach that contains the potential for identifying inaccuracies in ground-level perspectives and details (e.g., signs and pedestrian signals). In almost all cases, the postprocessing efforts and quality checks are a significant but necessary cost to ensure quality. Mobile mapping systems have been reported to be effective for automated data collection, and several recommendations have been developed to help states implement a MIRE MIS. The Figure 2. Data business planning process (Thompson and Scopatz 2017).
12 Integration of Roadway Safety Data from State and Local Sources recommendations include standardizing data collection methods to better ensure quality and enabling the transfer between information systems. In addition, FHWA recommends that both the public and private sectors explore automated techniques for extracting inventory feature data to a usable database format. Overall, standardized data will help support safety programs through data-driven decision making (Fiedler et al. 2013). Bureau of Indian Affairs A recent study published by the U.S. Government Accountability Office (GAO) provided a review of tribal issues related to current data accuracy by the Bureau of Indian Affairs (BIA). The focus of the review was to demonstrate how better data could improve roadway management and assist in strategies to increase Native American student school attendance (GAO 2017); however, there were some findings in the GAO report that pertain to this synthesis. For example, the study found that National Tribal Transportation Facility Inventory (NTTFI) data such as road location, length, and ownership are reasonably complete and accurate and therefore use- ful for identifying roads eligible for federal Tribal Transportation Program funding. It was also reported that electronic testing of NTTFI data on road description and condition (e.g., surface type, surface condition, and average daily traffic counts) found missing, inaccurate, and out- of-date entries but that FHWA uses the NTTFI data for reporting and oversight purposes. The study concluded that BIAâs guidance to tribes on how to code the data when entering it into NTTFI is unclear, which can result in inconsistent collection and outdated data and potentially lead to inaccuracies in budget justifications and performance reporting. For example, the data fields required by the NTTFI related to traffic counts (average daily traffic on major arterial roads) and surface condition (surface condition index) are outdated and may not be comparable across tribes. Finally, the study suggested that guidance provided by BIA does not require that data be updated on a routine basis and that condition data are not required to be collected in the same manner by all tribes. Data Access According to State of the Practice on Data Access, Sharing, and Integration (Vandervalk et al. 2016), FHWA began a 3-year project entitled âVirtual Data Access (VDA) Frameworkâ to develop a prototype framework to enable state and local transportation agencies to share planning and operations data from a variety of sources in a region. The project focused on using the VDA Framework to support planning for traffic operations; this support included the use of data from the framework as input to planning for operations analysis and simulation tools. The data that resulted from the study will be used to support the reporting of operational performance measures. The framework is being developed with proof-of-concept partners the Mid-America Regional Council and Kansas City Scout as well as with other stakeholders from across the United States. FHWA is developing a Performance Measurement and Analysis Tool (PMAT) that demonstrates one of the many options for using the integrated data from the VDA Framework. The VDA Framework and PMAT are being developed with open-source software and will be available to other regions. Vandervalk and colleagues (2016) established the type of data necessary to perform data-driven planning for operations activities and examined the planning for operations activities that currently use performance data. The report will be used to inform the framework design of what data should be inputted into the framework to support planning for operations. The article âWhat Drives Highway Safety Improvements?â (Gigliotti and Scurry 2016) emphasizes that data-driven and performance-based decisions are used to guide highway safety improvements. FHWA is currently developing new requirements for performance-based decision making that is data driven.
Literature Review 13 NCHRP Synthesis 367: Technologies for Improving Safety Data (Ogle 2007) presents methods for improving the six measures of safety data: timeliness, accuracy, completeness, comprehen- siveness, efficiency, and integration. The first step in this synthesis was to identify what safety analysis tools could be used to identify data requirements. Then, states were surveyed about their data requirements and what data collection and management technologies they had available. Furthermore, in-depth interviews with states helped extract additional information or clarify any questions. Next, a matrix of available technologies was developed, and a literature search was conducted on each technology on the list. Finally, the technologies were matched to specific data requirements for agencies looking to use new technologies for better safety information systems. The report concludes by highlighting the importance of flexibility and different combi- nations of technologies because a single technology is not sufficient to solve all safety data system cooperation and coordination problems. NCHRP Synthesis 460: Sharing Operations Data Among Agencies (Pack and Ivanov 2014) focuses on practices regarding sharing operational data between different agencies. Survey respondents represented a wide range of professionals, including staff from different divisions of several state DOTs, local agencies, and law enforcement and transit agencies. The survey reported that the majority of state DOTs shared some form of operational data with other agencies (i.e., providing or receiving data or both). The shared data were reported to be basic operational data elements such as speed measurements, crash types, and locations. Detailed operations data were less likely to be shared between agencies but would potentially present the most benefit. The research gaps identified were (1) quantification of the benefits and costs of data sharing to help promote data-sharing initiatives and (2) development of an economic analysis process based on agency size, agreement types, data-sharing method, and frequency. National Highway Traffic Safety Administration NHTSAâs traffic records assessments are peer evaluations of state traffic records system capabilities. States must complete a traffic records assessment once every 5 years to remain eligible for the State Traffic Safety Information System Improvements Grant [currently under the FAST Act; previously under MAP-21 and the Safe, Accountable, Flexible, Efficient Transpor- tation Equity Act: A Legacy for Users (SAFETEA-LU)]. Using the online State Traffic Records Assessment Program, independent subject matter experts examine state responses to a uniform set of questions and rate the responses against the ideal set forth in the Traffic Records Program Assessment Advisory (HS 811 644). States respond to the Advisory questions in three iterative rounds. For each response, states are rated as âmeets the advisory ideal,â âpartially meets the advisory ideal,â or âdoes not meet the advisory ideal.â The final report includes these ratings, recommendations, and considerations that the states may contemplate in working to improve their traffic records system performance. NHTSAâs Traffic Records GO Team program is designed to provide resources and assistance to state traffic records professionals as they work to better their traffic records data collection, management, and analysis capabilities. GO Teams are small groups of one to three subject matter experts designed to help states address traffic records issues. States may request training or technical assistance GO Teams that are small to medium in scope. The Model Minimum Uniform Crash Criteria (MMUCC) Guideline is a standardized data set for describing motor vehicle crashes and the vehicles, persons, and environment involved (NHTSA 2017a). The MMUCC is designed to generate the information necessary to improve highway safety within each state and nationally (NHTSA 2017b). Because the MMUCC is voluntary, states often use different formats and names for data elements and attributes, or they may combine (or split) MMUCC elements and attributes. As a result, it can be very difficult to
14 Integration of Roadway Safety Data from State and Local Sources compare or share crash data between states, between state and federal data sets, and, in some cases, even between different agencies within a state. To assist states in evaluating their consis- tency with the MMUCC, NHTSA and the Governors Highway Safety Association developed a methodology for mapping the data collected on crash reports and the data entered and main- tained on crash databases to the data elements and attributes in the MMUCC Guideline. This methodology is intended to standardize how states compare both their crash reports and their crash databases with the MMUCC. The process is referred to as âmapping to MMUCC.â Data Integration and Maintenance The intent of the MIRE MIS is to test the feasibility of integrating MIRE in safety manage- ment processes in state DOTs. Data managers are provided with an integrated safety data file structure in order to improve roadway safety performance. Some of the challenges that arise from the MIRE MIS include the quality of data, which can often be variable and difficult to interpret. Additionally, it is difficult to predict every requirement for the system before build- ing it. Finally, the cooperation of multiple organizations is required. Despite these challenges, the MIRE MIS provides a stand-alone system that is reported to be quicker and easier to use (Altobello et al. 2013). The Informational Guide for State, Tribal, and Local Safety Data Integration (Scopatz et al. 2016) is intended to supply states, tribal governments, and local agencies with a way to imple- ment safety data integration projects. This guide is useful for state DOTs, tribal transportation agencies, county and municipal governments, metropolitan planning organizations (MPOs), regional planning commissions (RPCs), and any researchers who support these agencies. It focuses on integrating safety data; however, other data sources such as asset management information on the condition and location of pavement, signs, markings, culverts, and other attributes of roadways may be included. Data integration includes both integration across jurisdictions and integration from various safety databases. Integration across jurisdictions occurs when DOTs, tribal governments, and other agencies agree to share data that support safety analyses. The goal of integration across jurisdictions is to describe how agencies can work together to improve data but not to dictate a single way of performing this task. Integration of safety databases allows agencies to accumulate their crash roadway inventory to create an analytic data resource. In doing so, agencies can more effectively work together to implement solutions to analyze safety more effectively on all public roads. FHWA published the Data Integration Primer (FHWA 2010), which defines data integration as âthe method by which multiple data sets from a variety of sources can be combined or linked to provide a more unified picture of what the data mean and how they can be applied to solve prob- lems and make informed decisions that relate to the stewardship of transportation infrastructure assetsâ (p. 9). State agencies rely on a large amount and wide variety of data for all phases of the construction process and to facilitate communication. While the decisions and the success of transportation asset management (TAM) are dependent on data integration, it is necessary that the data be accurate and updated such that applications can help in translating the data into a common transferable format. In addition, the Data Integration Primer discusses the benefits of data integration, such as integrated decision making, safety analysis, reduced duplication, quicker processing, enhanced program development with timely data, and greater accountability. It also provides a detailed discussion of the data integration process, including modeling, database design, database test- ing, training on the database, and implementation. The guide also lists several challenges of data integration, such as data quality and consistency, lack of storage capacity, unexpected costs, staff cooperation, and the perception of integration as an overwhelming effort.
Literature Review 15 NCHRP Synthesis 458: Roadway Safety Data Interoperability Between Local and State Agencies (Lefler 2014) provides an overview of the state of the practice on the interoperability of state and local safety data and highlights agency practices that support a data-driven safety program on all public roads. Interoperability is defined in the report as the ability of data, systems, or organizations to work together. This synthesis found that in terms of interoperability between state and local agencies, agencies were more advanced in the collection and use of crash data than of roadway or traffic data. It also found that many states were struggling to obtain safety data for local (non-state-owned) roadways to meet MAP-21 requirements (i.e., to incorporate local roadway data into a statewide base map and support analysis of that data). Local agencies are collecting some of the roadway data elements that DOTs are most interested in collecting, such as information on intersections, curves, and supplemental data sets (e.g., signs). NCHRP Synthesis 458 identified the need for support of data improvement efforts from both the DOT and local agency leadership. NCHRP Synthesis 508: Data Management and Governance Practices (Gharaibeh et al. 2017) broke down data management and government into four areas: data governance, data ware- housing and cloud computing, data integration and sharing, and data quality. Data governance should adopt a top-down approach to help recognize the value of data. Data warehousing and cloud computing services are expected to grow in the future, but most local agencies do not understand the magnitude of their growth. Also, these services require designated data stewards and staff. Data integration and sharing are limited by incompatible LRSs, but geographic mile points and route mile points are commonly used by the majority of LRS. According to the synthesis surveys, data quality is least evaluated by DOTs, while timeliness, accuracy, and security are most evaluated. Bureau of Indian Affairs The vitality of a comprehensive data system was discussed in a recent study published by the Mountain-Plains Consortium, Building a Sustainable GIS Framework for Supporting a Tribal Transportation Problem (Lee 2017). With the increase in oil extraction activities in North Dakota, the Fort Berthold Reservation in that state is experiencing a significant increase in both high- way and local road traffic. The reservation is home to Three Affiliated Tribes, or the Mandan, Hidatsa, and Arikara (MHA) Nation. To support the transport of energy product and resources and also to provide safe roads, the MHA Nation must plan cost-efficiently for both present and future traffic scenarios. The report stated that efficient planning can best be accomplished by: (1) integrating road networks to provide a comprehensive road network using multiple public sources and (2) providing guidance for performing quality control checks on data before delivering and using it for geospatial analysis. Tribal roads in South Dakota and North Dakota are evaluated for crash analysis, but the research reported that the attributes of tribal roads were separate from those of rural local roads maintained by other owners. However, tribal roads in North Dakota are not evaluated separately from state local roads. A safety toolkit was developed that contains a network screen- ing process from a safety perspective and includes all roads and intersections. A previous 2014 study demonstrated the process of merging data from two different public road networks (Choi et al. 2014). The Topologically Integrated Geographic Encoding and Referencing (TIGER) product for tribal transportation network modeling is currently used by the MHA Nation and on North Dakota roadway networks. The TIGER network has comprehensive links with all segments connected, but it does not provide road classification and surface type with details such as pavement type, travel speed, and the number of lanes. The workflow techniques proposed are transferrable to other tribal transportation agencies, and general guidance for agencies is provided. Once the road network is completed, the tool can be utilized in various ways, such as
16 Integration of Roadway Safety Data from State and Local Sources ambulance coverage analysis, tourism management, and logistics analysis. With the integrated road networks, the tribal transportation agencies can develop bike lane management, analysis of ambulance service coverage, truck-only lane management, road sign asset management, and road maintenance management. The study recommended that agencies develop LRSs on the proposed road network in order to adopt efficient asset management and version control. Data Management Roadway Data Improvement Program: Supplemental Information Resource (DeLucia et al. 2012) introduced resources to standardize and improve the quality of roadway data collected by trans- portation departments and agencies. Because safety countermeasures and initiatives require factual data to receive funding, transportation agencies need to know what data to collect, how to collect it, how to maintain it, and what it means. The Roadway Data Improvement Program provides the resources an agency can use to qualify data on the basis of its accuracy, completeness, consistency, integration, and accessibility. NCHRP Synthesis 446: Use of Advanced Geospatial Data, Tools, Technologies, and Information in Department of Transportation Projects (Olsen et al. 2013) provides an overview of the state of the practice regarding state DOTsâ use of advanced technology tools for storing data and summarizes the current state of the practice, including emerging standards and guidelines, throughout the United States as of 2012. This study also identified gaps in the research necessary to achieve the desired level of systems integration and to assist transportation agencies in developing a system- atic approach for adopting these technologies into a standard operating procedure. Implementation The All Road Network of Linear Referenced Data (ARNOLD) Reference Manual (FHWA 2014) provides guidance for the best practices and implementation of LRSs and GISs for transporta- tion. By using this manual, individual state DOTs and transportation agencies can meet FHWAâs Highway Performance Monitoring System (HPMS) requirement of a complete road inventory with linear-referenced networks. The manual is broken down into four steps to achieve com- prehensive LRS implementation: implementation planning, data collection and integration, building the LRS, and ongoing data maintenance. Key recommendations include collaboration with stakeholders, building an enterprise system to be used by many, using sustainable practices, expecting and managing change in both methods and data, and building the LRS incrementally as it grows. Creating effective LRSs and other visual databases out of robust data will support analyses that can be used to build the case for state and federal funding for enhanced roadway safety measures. The article âFHWA to Develop New Safety Data Management Guideâ lists seven steps for states to improve their safety data programs (Thompson 2016): 1. Plan for safety data management and governance; 2. Assess current state of safety data program; 3. Establish a safety data governance program; 4. Leverage technology for safety data management (e.g., business intelligence and information technology tools); 5. Develop implementation plan and assign priorities; 6. Document the safety data business plan; and, 7. Implement and sustain the safety data business plan (e.g., assign performance metrics to measure success).
Literature Review 17 Consistent and quality data are needed to define the framework for decision making in the design, operation, and safety of roadways. To address this need, FHWA developed approaches to helping states implement their highway safety improvement initiatives. It is easier to identify and fix safety problems when traffic and roadway data are incorporated in the process of implementing SHSPs. Many tools and methods that have been developed, such as the Highway Safety Manual (AASHTO 2010) and FHWAâs Interactive Highway Safety Design Model (FHWA 2017b), cannot function without roadway, traffic, and crash data. The end goal of fixing roadway problems needs both to be fiscally responsible and to directly target the improvement of safety (Tayal et al. 2016). FHWAâs Performance Measures for Roadway Inventory Data (Council et al. 2013) identified data quality issues as well as methods that assess roadway and traffic data that are collected and maintained. The development of performance metrics that assess MIRE data quality and MIS performance is addressed and suggestions are offered. Modifications such as rewording the current language to more clearly describe the measure, use of measures independently for state-system and local-system roads in more detail, and the adoption of additional measures are addressed. Data-related business practices, such as establishing a roadway inventory leader, calculating measures regularly, establishing performance goals, and developing a system of internal quality-control checks, are reported. It is reported that significant effort is required to incorporate MIRE into safety management programs because well-defined performance mea- sures, accepted by agencies, are needed in order to improve data quality. However, no standard procedures or measures have currently been agreed on. FHWA developed Transportation Asset Management (TAM), which is a strategic decision- making process for allocating resources to observe, plan, and execute. The aim of TAM is to create a systematic process for operating, maintaining, upgrading, and expanding physical assets throughout the life cycle of transportation facilities. The goal is to make the best decisions that optimize performance, along with resource distribution, on the basis of five core principles: policy-driven; performance-based; analysis of options and trade-offs; dependency on quality information for decisions; and constant monitoring to provide clear accountability and feed- back. These principles apply to every function within transportation agencies. Transportation Research Circular E-C196: Improving Safety Programs Through Data Governance and Data Business Planning (Hall 2015) reports on a survey of 10 states conducted to understand more details on the state of the practice on data management and data business planning solu- tions for safety applications. The DOTs in Alaska, Idaho, Iowa, Louisiana, Maryland, Michigan, Montana, Ohio, Rhode Island, and Washington State provided information on data governance, data management, data integration, and program assessment. Examples of meeting MAP-21 requirements were included through documentation of data processes, alignment of local and state data programs with national standards, and development of a long-term business plan for data programs. National Highway Traffic Safety Administration NHTSA evaluates traffic data to help identify problems and to design solutions for reducing the number of crash injuries and fatalities. In 2011, in collaboration with the Governors Highway Safety Association, NHTSA published a report entitled Model Performance Measures for State Traffic Records Systems that established measures intended for use by federal, state, and local governments to assess the development and implementation of traffic record data systems, strategic plans, and data-improvement grant processes. On the basis of the data attributes of timeliness, accuracy, completeness, uniformity, integration, and accessibility, the performance measures were assessed across six core state DOT traffic record data systems (i.e., crash, vehicle, driver, roadway, citation/adjudication, and emergency medical services/injury surveillance).
18 Integration of Roadway Safety Data from State and Local Sources NHTSAâs National Driver Register and Traffic Records Division uses guidance, outreach, standard practices, training, and technical assistance on data programs to coordinate the six core state systems. The aim of this effort is to improve timeliness, accuracy, uniformity, and accessibility of state traffic records systems. With the help of NHTSA, state agencies can improve their data collection, analysis, and management capabilities. Costs Related to MIRE FDEs FHWA report MIRE Fundamental Data Elements CostâBenefit Estimation (Tayal et al. 2016) explores the number of fatalities and injuries to be reduced in order to validate the costs of collecting data, as well as the available information regarding the cost of collecting these data elements when it was developed. The report indicates that conducting a costâbenefit analysis that considers cost effectiveness, is necessary for determining how much state agencies will spend on implementation. The analysis tabulated the average costs of data collection elements as $530 per mile, $720 per intersection, and $500 per ramp. The total cost estimates are influenced by whether the agency opts to collect data from all Federal-aid highways and non-Federal-aid roads or just from the Federal-aid highways. The MIRE MIS Lead Agency Program provided the majority of the funding information for data collection used in the Tayal et al. (2016) report. Some additional programs, such as the Utah DOTâs lidar effort, provided cost estimates as well. Cost information was classified into segment, intersection, ramp, and traffic volume. Annual data maintenance was also considered, and the project analysis period extended 16 years, from 2013 to 2029. It was reported that the benefits will take time to evaluate because a certain number of fatalities or injuries or both will need to be reduced in order to make the costs worth the expenditure. FHWAâs Driving Transportation Performance Through Data Management and Analytics (Redd and Van Hecke 2017) summarizes the challenge often faced by transportation agenciesâ both state DOTs and MPOsâof being forced to allocate limited funds to seemingly unlimited projects for long-term transportation investments that optimize performance. The traditional approach to making the most feasible choice includes finding information from multiple projects that is uniform and complete. This was reported to be a difficult task because data collection is typically outsourced to different agencies, a practice that can create inconsistencies in the reli- ability of the data from those sources. In addition, the information from many of these sources comes from previous projects (which could include up to a decadeâs worth of records), so that obtaining readily available information becomes a challenge. Sometimes, even if information is provided, there is no standardization in the selection process, which makes a decision more difficult in the end. The solution to this process is to implement new technologies in all steps, namely, data collection, data integration, and data management. Market Analysis of Collecting Fundamental Roadway Data Elements to Support the Highway Safety Improvement Program (Lefler et al. 2011) contains a literature review conducted to identify resources to develop a methodology for a costâbenefit analysis of improving highway safety. It showed there were no methodologies for estimating the benefit of collecting data for safety. An alternative approach gathered costs of data collection and determined the degree to which fatalities and injuries would have to be reduced to exceed costs, and determined the benefit on that basis. There are two scenarios, both of which are built around the state DOT developing a common statewide relational LRS, that are linkable with crash data. In the first scenario, FDE/HSIP data are collected on all Federal-aid highways. This means that, in addi- tion to the 16 elements already required for HPMS on Federal-aid highways, the state would have to collect the remaining 22 FDEs. The second scenario is the same as the first, except that it goes a step further to collect all 37 FDEs on all non-Federal-aid roads. It should be noted that
Literature Review 19 the HPMS does not currently require data collection of any FDE elements on non-Federal-aid roadways. Summary This comprehensive literature review revealed a few key findings regarding the various methods of collecting, accessing, and managing roadway safety data. First, the federal agencies have provided an abundance of guidance and information related to roadway safety data and elements that relate to or fulfill the requirements of the MIRE FDEs. Additionally, at the time this report was written, several states had efforts under way for collecting roadway safety data and for implementing systematic approaches to integrating, maintaining, and managing the data. The Esri suite of geo-based products and other advanced technologies (LRS, GIS) have been used more extensively for data collection. Most state DOTs are still accessing and managing roadway safety data in database tools (e.g., spreadsheets) but have plans for implementation of more robust integration and maintenance practices.
