Broadening Integrated Corridor Management Stakeholders (2020)

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A-1 A P P E N D I X A Characteristics of Recurrent and Non-Recurrent Congestion This appendix provides a data-driven foundation to understand and characterize the range of operational conditions that drive congestion patterns, corridor performance, and integrated management response. First, the guidance establishes the motivation for Integrated Corridor Management (ICM) stakeholders to define operational conditions rigorously using observed data to help create a consensus view on patterns of corridor congestion. Second, the guidance discusses how the data needed to support this effort can be collected, analyzed, and visualized. Third, the guidance provides lessons-learned and proposed processes to create practical and data- driven ICM response plans that are created through stakeholder interaction using data-driven characterizations of operational conditions. The goal is to provide the ICM stakeholders with the tools to right-size the chosen transportation management methods suited to the technical and institutional capabilities at hand—and set a course for improvement over time. Characterizing and Visualizing Operational Conditions This appendix provides a review of different methods for visualizing and communicating the severity, extent (temporal or spatial), nature, and/or characteristics of congestion, such as through color-coded network diagrams, GIS maps, speed contour plots, travel time reliability charts, vehicle trajectory plots (i.e., time-space vehicle plots), cumulative count curves, and other methods. An operational conditions analysis ingests several months of contemporaneous, time- dynamic travel time, bottleneck throughput, weather, incident, and travel demand data to create mutually exclusive and exhaustive set of similar conditions—and their frequency of occurrence. For example, an analysis of a corridor identified 30 distinct operational conditions, depicted in FIGURE A.1. The figure organizes the 30 conditions spatially based on increasing travel demand (along the x-axis) and disruptions to roadway supply (along the y-axis). Each condition is a collection FIGURE A.1. Visualizing operational conditions in the Seattle I-5 Corridor (Federal Highway Administration).

A-2 Broadening Integrated Corridor Management Stakeholders of four or more actual days, and the total size of the box representing the condition reflects its frequency of occurrence. In 2017, FHWA released a key document related to the systematic identification of operational conditions— developed and authored by Cambridge Systematics Team members. This document is a guide on the systematic integration of data and analytic resources into transportation systems management, Scoping and Conducting Data-Driven 21st Century Transportation System Analyses. This guidebook provides a wealth of material to reference and draw upon for the ICM community, and this appendix in the NCHRP 03-121 Guidebook draws heavily from the 21st Century Guide. Further, the basic concepts of the ICM AMS Guide are highly consistent and complementary with these materials. The goal of the ICM-specific guidance is not to parrot these materials, but to put the statistical methods and data management processes detailed in each FHWA guidebook into context for the ICM stakeholder. ICM focuses on various multimodal travel scenarios under varying operational conditions, in particular both recurrent and non-recurrent traffic congestion. A corridor’s non-recurrent congestion scenarios entail combinations of demand increases and capacity decreases. The overall premise is that key ICM impacts may be lost if only “normal” travel conditions are considered. The ICM scenarios take into account both average- and high-travel demand within the corridor, with and without incidents. The relative frequency of non-recurrent operational conditions (i.e., incidents or other significant non-recurrent operational conditions that affect corridor performance such as work zones, special events, weather, etc.) is also important to estimate (based on archived traffic conditions) in this process. While ICM is designed to address both recurrent and non-recurrent events, the post-deployment evaluation of the two demonstration sites (Dallas and San Diego) focused solely on incident- or congestion-related events. The potential ICM deployment-related alternatives were identified using cluster analysis that grouped together incidents or congestion events that occurred under operational conditions (e.g., time of day, direction of traffic, length of time until the incident was cleared, etc.) which were more similar to each other, than to those in other groups (clusters). These clusters were then prioritized based on total delay impact. Data and tools can be brought together to provide increasingly robust and quantitative measures of system performance. Some useful measures of system products over some time (e.g., a peak period, or a day, or a month) may include reliably completed trips and total value of goods delivered. These may be hard to measure directly. However, using time-variant travel time data and supporting estimates of ridership and volume data, travel reliability analysis can be conducted that is a key first step in the measurement of system product. Reliability data are a key element in characterizing trip-making since, if a trip takes much longer than expected, the dis-benefits associated with disrupting travel plans outweigh the benefits. This is particularly true for goods movement within a supply chain, but the same basic principles hold for person- trips. For example, if a trip home from work takes so much longer than expected that changes to childcare arrangements are required, this often has direct and measureable financial consequences. The purpose of building system profiles is to characterize system performance (i.e., the system is getting better or worse) and to identify what are missing in the profile so the profile can be improved in the long term. This section illustrates a series of example system profiles (including their key components), including congestion profiles, reliability profiles, and safety profiles. Congestion Profile Time-variant congestion measures may contain travel time, vehicle delay, bottleneck throughput, queue length, vehicle stops and other attributes depending on the nature of the problem and the system features. For example, to create a freeway corridor congestion profile, travel time and bottleneck throughput may be selected as performance measures; to analyze an intersection performance, queue length and vehicle delay may be used throughout the study. FIGURE A.2 presents an example of congestion maps from Washington

Characteristics of Recurrent and Non-Recurrent Congestion A-3 Department of Transportation (WSDOT).1 Using speed data, WSDOT has archived a system-wide congestion map on 10-minute intervals since January 2013. These maps can be used in sequence in a simple and intuitive way to identify problem/bottleneck locations as well as other changes within the system over time. If local agencies are interested in exploring opportunities to divert freeway or arterial traffic to transit, they will engage in collaborative dialogue with the regional transit managers to understand possibilities for creating available transit capacity, including parking facilities, to accommodate the possible influx of demand under certain scenarios. FIGURE A.3 presents example comparisons of expected changes in delays stakeholders visually assess expected improvements and reductions in service in different parts of the network. FIGURE A.2. Washington State Department of Transportation congestion maps (WSDOT). 1 http://www.wsdot.wa.gov/data/tools/traffic/maps/archive/?MapName=SysVert. in a transportation network that contains both freeway and arterial segments; such comparisons help

A-4 Broadening Integrated Corridor Management Stakeholders FIGURE A.3. Comparison of change in delays (Cambridge Systematics). Reliability Profile A within-day time-variant travel time chart is an effective way to convey systemwide or individual route travel time reliability. When combined with the number of delivered trips these data can also be used to measure and report reliable throughput. The FHWA Travel Time Reliability Measures Guidance2 document suggests a set of performance measures used to quantify travel time reliability: 90th or 95th percentile travel time, buffer index, planning time index, and frequency that congestion exceeds some expected threshold (as shown in FIGURE A.4). According to the guide, travel time reliability measures the extent of this unexpected delay and provides this definition: the consistency or dependability in travel times, as measured from day-to-day and/or across different times of the day. 2 http://ops.fhwa.dot.gov/publications/tt_reliability/.

Characteristics of Recurrent and Non-Recurrent Congestion A-5 FIGURE A.4. Reliability measures are related to average congestion measures (USDOT). Safety Profile Common safety measures include crash rates and number of fatalities, which can be acquired directly from state or local agency databases. Similar to a congestion profile, a geographic illustration can be an effective way to identify the problem locations. In the KC Scout Annual Report3, as shown in FIGURE A.5, KC Scout uses a heat map to illustrate the locations of multiple-vehicle incidents in 2013 to identify severe/high incident rate locations intuitively. 3 KC Scout FY 2013 Annual Report. http://www.kcscout.net/downloads/Reports/Annual/AnnualReport2013.pdf.

A-6 Broadening Integrated Corridor Management Stakeholders FIGURE A.5. Top multi-vehicle incident locations by route (KC Scout). FIGURE A.6 and FIGURE A.7 depict accident per million vehicle miles and accident rates by location for a California freeway. FIGURE A.6. Accident rates in space and in time (Cambridge Systematics).

Characteristics of Recurrent and Non-Recurrent Congestion A-7 FIGURE A.7. Accident rates by location (Cambridge Systematics). Observations about the performance of the system can come from many different viewpoints: from system managers who observe the buildup and dissipation of congestion through graphical displays and camera images, from agency staff who maintain the system, from the public safety officers who traverse the system, and from travelers themselves. When something is not “right” or when system dynamics change, there are likely stakeholders within the system who will note that something out of the ordinary is happening. Whether this insight comes from direct, hypothesis-oriented engagement with data or the experience observing or traversing the transportation system, it is important to establish processes and relationships that pull together insights from diverse stakeholders. Combining insights from individual observations and the analysis of data can lead to powerful and effective outcomes with respect to understanding system dynamics. Key Performance Measures and Data Needs Refer to Appendix D for definitions, geographic and temporal scales, and relevant statistical tests of key performance measures. Refer to Appendix F for an example data collection plan used for the San Diego I-15 ICM project. Performance measurement has received recent attention as a focal point for improved transportation system management. The FHWA Office of Transportation Performance Management4 offers a website with resources intended to assist transportation system managers in creating and refining systemwide Transportation Performance Management (TPM) capabilities as shown in FIGURE A.8. Many agencies are working to enhance their TPM capabilities in advance of expected national rulemaking related to performance management. 4 http://www.fhwa.dot.gov/tpm/.

A-8 Broadening Integrated Corridor Management Stakeholders FIGURE A.8. Definition of transportation performance management (USDOT). The primary function of a transportation system is to facilitate the movement of people and goods. The inherently positive “products” of the system are completed trips, goods moved from one place to another, and travelers delivered to their destinations. Early in an ICM project, the project team must define performance measures in line with the project objectives, mitigation strategies under consideration, and operational conditions (shaped by the understanding of available data) identified for the project. In order to begin to understand how the proposed transportation improvements will perform and whether they will meet stakeholder expectations, and even to help stakeholders develop realistic expectations for the improvements, project managers must first be willing to articulate them. Performance measures should be closely tied to the identified overall project goals and objectives and the expected traveler responses. For many improvement strategies, it is important to consider a set of performance measures that are sensitive to recurring as well as non-recurring congestion. An effective way to identify appropriate performance measures is to develop one or more specific hypotheses to be tested for each objective. These hypotheses either can indicate a change in travel conditions, such as, “The strategies will reduce travel times during an incident by five percent,” or can be neutral in the prediction of an impact, such as “The strategies will not result in a change in emissions rates.” Performance measures that support the testing of the formulated hypothesis should then be identified. Use of this method ensures that the performance measures are appropriately mapped to the project goals and objectives. To be able to compare different investments within a study area, it will be important to define and apply a consistent set of performance measures. The performance measures should: Provide an understanding of travel conditions in the study area including both localized and system- wide metrics representing impacts in both the immediate vicinity of the proposed improvement as well as representing impacts in the larger study area. Be consistent with lead agency overall performance measures used to evaluate all sorts of transportation improvements. TPM, a strategic approach that uses system information to make investment and policy decisions to achieve national performance goals… Is systematically applied, a regular ongoing process Provides key information to help decision makers allowing them to understand the consequences of investment decisions across multiple markets Improving communications between decision makers, stakeholders and the traveling public. Ensuring targets and measures are developed in cooperative partnerships and based on data and objective information FHWA De�inition of Transportation Performance Management

Characteristics of Recurrent and Non-Recurrent Congestion A-9 Demonstrate the ability of improvement strategies to improve mobility, throughput, and travel reliability based on current and future conditions. Help prioritize individual investments or investment packages within the study area. To the extent possible, the measures selected should be reported for the overall system and by: Mode—Single-Occupancy Vehicles (SOV), High-Occupancy Vehicles (HOV), transit, freight, non-motorized, etc. Facility Type—Freeways, expressways, arterials, local streets, etc. Jurisdiction—Region, county, city, neighborhood, and study area-wide. Performance measures will typically focus on the following key areas described below. However, customized measures may be selected based on unique impacts of individual mitigation strategies. The key performance areas are: Mobility. Mobility describes how well people and freight move in the study area. Mobility performance measures are readily forecast. Three primary types of measures are used to quantify mobility, including travel time, delay, and throughput. Travel time and delay are straightforward to calculate. Throughput is calculated by comparing travel times under the incident scenarios to those under no incident—by comparing the percentage of trips under the same threshold travel time in both the pre- and post-mitigation scenarios, the relative influence of the strategies on reducing extreme travel times can be estimated. Reliability and Variability of Travel Time. Reliability and variability capture the relative predictability of the public’s travel time. Unlike mobility, which measures how many people are moving at what rate, the reliability/variability measures focus on how mobility varies from day to day. Travel time reliability/variability is typically reported in terms of changes in the Planning Index and changes in the standard deviation of travel time. Transportation Safety is another performance area that may be of interest to transportation analysis. Safety is typically measured in terms of accidents or crashes in the study area, including fatalities, injuries and property damage only accident. Currently available safety analysis and prediction methodologies are not sensitive to transportation improvement strategies. At best, available safety analysis methods rely on crude measures such as V/C, or rely on empirical comparison methods such as identifying safety benefits resulting from the implementation of a certain type of mitigation strategy and then applying the same expected improvement rate to a future implementation of the same or similar strategy. Clearly, this is an area deserving new research. Emissions and Fuel Consumption. Emissions and fuel consumption rates are used to produce estimates based on variables such as facility type, vehicle mix, and travel speed. Cost Estimation. Planning-level cost estimates include life-cycle costs including capital, operating, and maintenance costs. Costs are typically expressed in terms of the net present value of various components. Annualized costs represent the average annual expenditure that is expected in order to deploy, operate, and maintain the transportation improvement and replace equipment as they reach the end of their useful life.

A-10 Broadening Integrated Corridor Management Stakeholders Data Requirements The precise data requirements for calculating performance measures will vary depending on the performance measure; however, they all require basic inputs, including: Roadway geometry. Traffic control data. Travel demand, traffic volumes, and intersection turning movements. Performance data, such as queue locations, queue lengths, travel times, and speeds. Data on vehicle characteristics, such as vehicle classifications or vehicle mix. The data requirements should be evaluated in the early inception stages of a project to get an early estimate evaluations of the data (Margin of Error) can prove highly valuable for subsequent development of effort required. Often, it is possible to use existing data, but these data may be outdated or from different timeframes for different parts of the network. In that case, resources need to be allocated for new data collection. If there is limited funding, resources need to be spent judiciously for collection of sufficient, quality data to conduct a study that would help inform decision-makers of the potential implications of their proposed transportation investments. Data sources include the following: Travel Demand—The basic demand data needed are the entry volumes (i.e., the travel demand entering the study area) at different points of the network. At intersections, the turning volumes or percentages should be specified. Origin-Destination (O-D)—O-D trips can be estimated from a combination of travel demand model trip tables and from traffic counts. O-D data can be acquired from the local Metropolitan Planning Organization’s (MPO) regional travel demand model. License plate matching surveys can be used to estimate hourly trips, but this is resource intensive. If the study area has transit, HOV and trucks in the vehicle mix, or if there is significant interaction with bicycles and pedestrians, the corresponding demand data would be needed. Vehicle Characteristics—Vehicle characteristics data can be obtained from the state DOT or air quality management agency, while National data can be obtained from car manufacturers, the Environmental Protection Agency (EPA), and the FHWA. Traffic Control Data—Data from traffic control devices at intersections or junctions are required. Control data refer to the type of control device (e.g., traffic signal, stop sign, ramp meter, etc.), the locations of these control devices, and the signal timing plans. Traffic control data can be obtained from the agencies that operate the traffic control devices in the given study area. Traffic operations and management data on links also are needed. These include location and type of warning signs— for lane drops, exits, guide signing; type and location of regulatory signs. If there are HOV lanes, information on the HOV lane requirement (HOV-2 versus HOV-3), their hours of operation, and the location of signs are needed; if there are HOT lanes, information on the pricing strategy is required. of effort required. Quality/variability of existing data will affect sample sizes; therefore, early statistical

Characteristics of Recurrent and Non-Recurrent Congestion A-11 Operational Conditions—If there are Variable Message Signs (VMS) in the study area, the type of information that is displayed, the location, and if possible, the actual messages that were displayed are needed. Most of this information can be obtained from the public agencies responsible for operating the VMS. The type of signs and locations can be obtained from GIS files, aerial photographs, and construction drawings. Event data can be received from public agencies, such as the Traffic Management Center (TMC) logs. Crash databases should be verified since data may not always be recent and may not be for the specific study area. In addition, work zone data are usually available from State DOTs and weather data are available from the National Oceanic and Atmospheric Administration (NOAA). Transit Data—Transit data can be obtained from the local and regional transit operators—these data can include schedules and stop locations. Calibration data could include Transit AVL data, boarding and alighting data, and dwell time at stops. Mobile Source Data—Mobile source data include data derived from mobile phones, bluetooth devices, and other mobile sources—these can be used to augment other data collected. The primary type of data obtained from mobile sources are speeds and travel times. In most cases, the mobile source techniques use samples of vehicles in the traffic stream, but they may not be reliable sources for traffic counts and vehicle composition. The mobile source data are typically obtained, stored and sold by private vendors. Going through a systematic process of collecting the critical data, verifying data quality, and documenting any assumptions are key to justifying the results of a study to decision-makers and the public. A statistical analysis of collected and previously available data can be helpful in determining the statistical data variability and the margin of error contained in the data. Typical challenges with data include: Data Comprehensiveness—Comprehensive data cover different performance measures (volumes, speeds, bottlenecks, queuing, and congestion data) across freeways and arterial streets, as well as transit data and incident data. Traffic counts should be taken at key locations in the study area— key locations include major facilities (freeway segments, major intersections and interchanges, and major on- and off-ramps). If possible, data collection should be done simultaneously at all key locations. Otherwise, the counts should be taken at least during similar timeframes with similar demand patterns and weather conditions. Data Reliability—Automated data sources are often best for collecting the long-term data necessary to calculate reliability metrics; however, many existing automated data collection systems lack the robustness or reliability to compile relevant data sets effectively. A thorough assessment of the data quality from all sources is recommended to identify any potential problems early on in the process and establish methods to address any deficiencies. Accuracy is the measure of the degree of agreement between data values and a source assumed to be correct. It also is defined as a qualitative assessment of error. It is important to have accurate, internally consistent, and recent data. With respect to data filtering and fusion, it is crucial to adopt standard ways to accept or reject field data, and to address data gaps and missing data. A small Margin of Error in the collected data is required to increase the validity of analysis results. It is necessary to collect enough data points for each performance measure (volumes, speeds, etc.) so that the sample is an accurate representation of the mean and standard deviation of this performance measure.

A-12 Broadening Integrated Corridor Management Stakeholders Depending on the mean to standard deviation ratio and the desired margin of error, the required sample size may vary greatly. Analysis and Evaluation Methods This section includes a discussion of several common methods for evaluating the extent, severity, and/or impact of recurrent and non-recurrent congestion, along with methods for analyzing their causes. Unlike traditional corridor studies, which often focus on a specific element of a corridor (i.e., a freeway or freeway and frontage road during a specific time of day), ICM analysis is a comprehensive approach that analyzes different operational conditions across time and transportation modes and across a large enough geographic area to absorb all impacts. The complexity involved in ICM analysis goes far beyond what is typically required for more traditional types of transportation investments. The potential inclusion of multiple facility types (freeway and arterial) and multiple transportation modes, combined with the potential for road use pricing influences, complicates the analysis. The focus of the ICM strategies on non-typical operational scenarios (e.g., high demand, incidents, and inclement weather) adds further complexity to the assessment. Finally, the ICM analysis methodology enables a more sensitive analysis of corridor-level performance. Traditional travel demand models are sufficient for analyzing the impacts of major infrastructure investments, such as new freeways. However, when agencies are interested in fine-tuning transportation operations strategies to produce systemwide improvements that optimize existing infrastructure performance, they need time- and space- dynamic tools that are more sensitive and that enable insight into the benefits that are otherwise too marginal to see in traditional modeling. One of the defining features of the ICM analysis methodology is that it enables agencies to understand system dynamics at the corridor level. The ICM analysis methodology uses corridor-level performance metrics in addition to the facility-level metrics to evaluate and understand corridor performance. The ICM analysis methodology accomplishes this through the combined use of multiple classes of available modeling tools. By combining aspects of macroscopic simulation (i.e., travel demand modeling (TDM), good for analyzing implications associated with mode shift), mesoscopic simulation (utilized to analyze regional strategies such as traveler information and pricing), and microscopic simulation (ideal for analyzing traffic control strategies), the ICM analysis methodology enables robust hypothesis modeling under a range of operating conditions of interest to the corridor for more informed decision-making. This produces improved analysis as compared to travel demand models alone because the combined tools yield more accurate travel times and speeds through the corridor, more in-depth understanding of bottleneck locations and their root causes, and an understanding of the influences beyond the periphery of the corridor that underlie corridor demand. The use of the different models allows specific strengths of the individual models to be combined: travel demand models provide estimates of long-term travel demand changes resulting from capacity changes, while more focused meso- and microsimulation models assess short-term operational impacts during specific non-recurring congestion conditions. The ICM analysis approach should be implemented in conjunction with the ICM system development and conduct of ICM analysis also supports continuous improvement of the supporting ICM system. As the analysis process continues in parallel with the ICM system development and design process, it is likely that new strategies, alternatives and scenarios will emerge that will need to be evaluated within the analysis process; therefore, the flexibility to foresee and account for several iterations of analysis is critical. The design process may reveal new strategies or alternatives that may need to be analyzed, prompting modifications to the analysis structure. Likewise, the ICM analysis process may reveal parts of the concept design process to provide a tool for continuous improvement of corridor performance. Regular periodic

Characteristics of Recurrent and Non-Recurrent Congestion A-13 of operations that are unworkable or uncover opportunities that may be leveraged that result in changes to the ultimate ICM design. The Value of Integrated Corridor Management Analysis Investing in ICM analysis is a major undertaking that requires stakeholders to agree to the value proposition. Certainly, the ICM analysis methodology can provide valuable insight into the potential cost-benefits of ICM. The specific cost-benefit analysis results will vary by corridor. However, the general value of conducting ICM analysis is the extent to which it assists corridor stakeholders implementing ICM to Invest in the right strategies—ICM analysis offers corridor managers a predictive forecasting capability that they lack today to help them determine which combinations of ICM strategies are likely to be most effective and under which conditions: Analysis helps decision-makers identify technical and implementation gaps, evaluate ICM strategies, and invest in the combination of strategies that would most minimize congestion and produce the greatest benefits. Comprehensive modeling increases the likelihood of ICM success and helps minimize the unintended consequences of applying ICM strategies to a corridor. It provides an enhanced understanding of existing corridor conditions and deficiencies, allowing the improved ability to match and configure proposed ICM strategies to the situation at hand. Invest with confidence—ICM analysis allows corridor managers to “see around the corner” and discover optimum combinations of ICM strategies, as well as potential conflicts or unintended consequences inherent in certain combinations of strategies that would otherwise be unknowable before full implementation: Analysis helps managers estimate the benefits resulting from ICM across different transportation modes and traffic control systems. Importantly, it helps managers to align these estimates with specific assumptions about corridor conditions and ICM strategies. Without being able to predict the effects of ICM strategies, corridor transportation agencies may not take the risk of making the institutional and operational changes needed to optimize corridor operations. Lower risk associated with implementation—Analysis facilitates the detailed development of concepts of operations and requirements by stakeholders, and helps corridor managers define and communicate key analysis questions, project scope, partner roles, and partner responsibilities. The analysis facilitates the development of concepts of operations and requirements by stakeholders in more detail, and helps corridor managers understand in advance what questions to ask about their system and potential combinations of strategies to make any implementation more successful: The development of an ICM analysis plan may help identify flaws or technical issues in the Implementation Plan or Concept of Operations (CONOPS) that may have been otherwise overlooked. The ICM analysis helps to communicate the scope of the project and appropriately set expectations among differing project stakeholders (e.g., planners, operators, data analysts, modelers, and agency management from State, local, and/or regional transportation agencies), and provides a clearer definition of expected roles and responsibilities. Analysis also helps managers identify and prioritize resources to project objectives, allowing for the effective and efficient allocation of resources and more sound project management.

A-14 Broadening Integrated Corridor Management Stakeholders The Integrated Corridor Management Analysis Process FIGURE A.9 presents the five major work steps, summarized below, associated with implementing the Integrated Corridor Management (ICM) Analysis, Modeling, and Simulation (AMS) methodology, as specified in the FHWA Traffic Analysis Tools Guide Volume XIII. This appendix of the ICM Guidebook borrows heavily from the ICM AMS Guide. Refer to Appendix E for more details on each work step. The five ICM analysis work steps include: 1. Develop Analysis Plan—The analysis plans provide a valuable tool for communicating the scope of the project. The analysis plan typically includes initial planning and scoping and then iterative updates to assumptions, scope, and agreements as the project moves forward. The development of the analysis plan is the primary mechanism for securing a clear and mutual understanding among stakeholders of expectations and assumptions. It may help to identify flaws or technical issues in the ICM Concept of Operations (CONOPS) that may have been otherwise overlooked.The analysis plan confirms not only the stakeholder agreements regarding the scope of the ICM analysis, but also the most appropriate approach to the analysis based on an enhanced understanding of project objectives, the corridor conditions, the ICM strategies being implemented, and the available tools and data. The contents of the Analysis Plan include: Project definition, Geographic and temporal scope, Selecting the appropriate analysis tool, Performance measures to be used in the analysis, Data requirements, Analysis tool calibration criteria and expectations, Alternatives to be studied including analysis scenarios and transportation mitigation strategies, and Expected cost, schedule and responsibilities for the analysis. The benefits of completing this work step include a better allocation of resources appropriate to the study objectives; a clear and shared understanding of roles, responsibilities, and expectations among project participants; and the ability of project participants to FIGURE A.9. Integrated Corridor Management Analysis, Modeling, and Simulation Approach Work Steps (Office of the Assistant Secretary for Research and Technology and Cambridge Systematics, Inc., 2017).

Characteristics of Recurrent and Non-Recurrent Congestion A-15 communicate the project vision to the broader stakeholders effectively. It also helps maintain agreement and project continuity as stakeholders leave positions and new staff comes in mid-stream. 2. Develop Data Collection Plan and Collect Data—The purpose of this work step is to collect the needed data to support the desired analysis cost effectively. In this step, project partners research data needs and availability, identify available data as well as gaps and methods to address those gaps where possible, compile and archive needed data, collect data, and perform quality control on the collected data. The successful completion of this task will support high confidence in AMS through the collection of appropriate, high-quality data using the most effective and efficient methods. Doing this well can substantially reduce costs both downstream and in continual process improvement. 3. Model Setup and Calibration—The purpose of this step is to configure the model(s) and analysis tools to reflect the agreed-upon objectives, scope, and parameters of the analysis and to verify proper model calibration to support accurate results. In this step, the baseline model network is developed, including all relevant transportation facilities and modes. In addition, baseline demand modeling is conducted, and the simulation models are calibrated. This step also includes testing sensitivity of the model to understand limitations of the analysis better. This work step can often be the most time- and resource-demanding of the AMS process. Successful completion of this work step will ensure the integrity of the developed models and the efficient use of valuable resources, and will support risk management for this critical step. 4. Alternatives Analysis and Documentation—The purpose of this step is to identify the optimum combination of ICM strategies for various operational conditions (e.g., varying roadway congestion and transit demand levels; incident conditions; weather conditions affecting operations; presence of work zones; special events) to support effective ICM. This step includes developing future baseline model networks and trip tables for all operational conditions and conducting the alternatives analysis for all ICM strategies. This step assumes that preliminary strategies/alternatives screening has already been performed using sketch planning or other iterative examinations. The outcomes of this project will include an understanding of predicted effects (including unintended consequences) for various hypotheses of interest, prioritization of ICM alternatives, and a quantified understanding of project benefits and costs. The results will inform ICM deployment decisions and can help build support among broader stakeholders for the ICM system. 5. Continuous Improvement—In this step, practitioners reassess models, model calibrations, and results against observed conditions to validate the analysis approach. Lessons learned are used to improve the process for future deployments, and ongoing performance measurement is used to refine the efficiency of the ICM. This step is ongoing, and consists of the repetition of this process in a manner that reflects and incorporates the data gathering and lessons learned from previous steps.