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Suggested Citation:"Cases." National Academies of Sciences, Engineering, and Medicine. 2019. Management and Use of Data for Transportation Performance Management: Guide for Practitioners. Washington, DC: The National Academies Press. doi: 10.17226/25462.
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Suggested Citation:"Cases." National Academies of Sciences, Engineering, and Medicine. 2019. Management and Use of Data for Transportation Performance Management: Guide for Practitioners. Washington, DC: The National Academies Press. doi: 10.17226/25462.
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Suggested Citation:"Cases." National Academies of Sciences, Engineering, and Medicine. 2019. Management and Use of Data for Transportation Performance Management: Guide for Practitioners. Washington, DC: The National Academies Press. doi: 10.17226/25462.
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Suggested Citation:"Cases." National Academies of Sciences, Engineering, and Medicine. 2019. Management and Use of Data for Transportation Performance Management: Guide for Practitioners. Washington, DC: The National Academies Press. doi: 10.17226/25462.
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Suggested Citation:"Cases." National Academies of Sciences, Engineering, and Medicine. 2019. Management and Use of Data for Transportation Performance Management: Guide for Practitioners. Washington, DC: The National Academies Press. doi: 10.17226/25462.
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Suggested Citation:"Cases." National Academies of Sciences, Engineering, and Medicine. 2019. Management and Use of Data for Transportation Performance Management: Guide for Practitioners. Washington, DC: The National Academies Press. doi: 10.17226/25462.
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Suggested Citation:"Cases." National Academies of Sciences, Engineering, and Medicine. 2019. Management and Use of Data for Transportation Performance Management: Guide for Practitioners. Washington, DC: The National Academies Press. doi: 10.17226/25462.
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Suggested Citation:"Cases." National Academies of Sciences, Engineering, and Medicine. 2019. Management and Use of Data for Transportation Performance Management: Guide for Practitioners. Washington, DC: The National Academies Press. doi: 10.17226/25462.
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Suggested Citation:"Cases." National Academies of Sciences, Engineering, and Medicine. 2019. Management and Use of Data for Transportation Performance Management: Guide for Practitioners. Washington, DC: The National Academies Press. doi: 10.17226/25462.
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Suggested Citation:"Cases." National Academies of Sciences, Engineering, and Medicine. 2019. Management and Use of Data for Transportation Performance Management: Guide for Practitioners. Washington, DC: The National Academies Press. doi: 10.17226/25462.
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Suggested Citation:"Cases." National Academies of Sciences, Engineering, and Medicine. 2019. Management and Use of Data for Transportation Performance Management: Guide for Practitioners. Washington, DC: The National Academies Press. doi: 10.17226/25462.
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Suggested Citation:"Cases." National Academies of Sciences, Engineering, and Medicine. 2019. Management and Use of Data for Transportation Performance Management: Guide for Practitioners. Washington, DC: The National Academies Press. doi: 10.17226/25462.
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Suggested Citation:"Cases." National Academies of Sciences, Engineering, and Medicine. 2019. Management and Use of Data for Transportation Performance Management: Guide for Practitioners. Washington, DC: The National Academies Press. doi: 10.17226/25462.
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Suggested Citation:"Cases." National Academies of Sciences, Engineering, and Medicine. 2019. Management and Use of Data for Transportation Performance Management: Guide for Practitioners. Washington, DC: The National Academies Press. doi: 10.17226/25462.
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Suggested Citation:"Cases." National Academies of Sciences, Engineering, and Medicine. 2019. Management and Use of Data for Transportation Performance Management: Guide for Practitioners. Washington, DC: The National Academies Press. doi: 10.17226/25462.
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Suggested Citation:"Cases." National Academies of Sciences, Engineering, and Medicine. 2019. Management and Use of Data for Transportation Performance Management: Guide for Practitioners. Washington, DC: The National Academies Press. doi: 10.17226/25462.
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Suggested Citation:"Cases." National Academies of Sciences, Engineering, and Medicine. 2019. Management and Use of Data for Transportation Performance Management: Guide for Practitioners. Washington, DC: The National Academies Press. doi: 10.17226/25462.
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Suggested Citation:"Cases." National Academies of Sciences, Engineering, and Medicine. 2019. Management and Use of Data for Transportation Performance Management: Guide for Practitioners. Washington, DC: The National Academies Press. doi: 10.17226/25462.
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Suggested Citation:"Cases." National Academies of Sciences, Engineering, and Medicine. 2019. Management and Use of Data for Transportation Performance Management: Guide for Practitioners. Washington, DC: The National Academies Press. doi: 10.17226/25462.
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Suggested Citation:"Cases." National Academies of Sciences, Engineering, and Medicine. 2019. Management and Use of Data for Transportation Performance Management: Guide for Practitioners. Washington, DC: The National Academies Press. doi: 10.17226/25462.
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Suggested Citation:"Cases." National Academies of Sciences, Engineering, and Medicine. 2019. Management and Use of Data for Transportation Performance Management: Guide for Practitioners. Washington, DC: The National Academies Press. doi: 10.17226/25462.
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Suggested Citation:"Cases." National Academies of Sciences, Engineering, and Medicine. 2019. Management and Use of Data for Transportation Performance Management: Guide for Practitioners. Washington, DC: The National Academies Press. doi: 10.17226/25462.
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Suggested Citation:"Cases." National Academies of Sciences, Engineering, and Medicine. 2019. Management and Use of Data for Transportation Performance Management: Guide for Practitioners. Washington, DC: The National Academies Press. doi: 10.17226/25462.
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Suggested Citation:"Cases." National Academies of Sciences, Engineering, and Medicine. 2019. Management and Use of Data for Transportation Performance Management: Guide for Practitioners. Washington, DC: The National Academies Press. doi: 10.17226/25462.
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Suggested Citation:"Cases." National Academies of Sciences, Engineering, and Medicine. 2019. Management and Use of Data for Transportation Performance Management: Guide for Practitioners. Washington, DC: The National Academies Press. doi: 10.17226/25462.
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Suggested Citation:"Cases." National Academies of Sciences, Engineering, and Medicine. 2019. Management and Use of Data for Transportation Performance Management: Guide for Practitioners. Washington, DC: The National Academies Press. doi: 10.17226/25462.
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Suggested Citation:"Cases." National Academies of Sciences, Engineering, and Medicine. 2019. Management and Use of Data for Transportation Performance Management: Guide for Practitioners. Washington, DC: The National Academies Press. doi: 10.17226/25462.
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Suggested Citation:"Cases." National Academies of Sciences, Engineering, and Medicine. 2019. Management and Use of Data for Transportation Performance Management: Guide for Practitioners. Washington, DC: The National Academies Press. doi: 10.17226/25462.
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Suggested Citation:"Cases." National Academies of Sciences, Engineering, and Medicine. 2019. Management and Use of Data for Transportation Performance Management: Guide for Practitioners. Washington, DC: The National Academies Press. doi: 10.17226/25462.
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Suggested Citation:"Cases." National Academies of Sciences, Engineering, and Medicine. 2019. Management and Use of Data for Transportation Performance Management: Guide for Practitioners. Washington, DC: The National Academies Press. doi: 10.17226/25462.
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Suggested Citation:"Cases." National Academies of Sciences, Engineering, and Medicine. 2019. Management and Use of Data for Transportation Performance Management: Guide for Practitioners. Washington, DC: The National Academies Press. doi: 10.17226/25462.
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Suggested Citation:"Cases." National Academies of Sciences, Engineering, and Medicine. 2019. Management and Use of Data for Transportation Performance Management: Guide for Practitioners. Washington, DC: The National Academies Press. doi: 10.17226/25462.
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Suggested Citation:"Cases." National Academies of Sciences, Engineering, and Medicine. 2019. Management and Use of Data for Transportation Performance Management: Guide for Practitioners. Washington, DC: The National Academies Press. doi: 10.17226/25462.
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Suggested Citation:"Cases." National Academies of Sciences, Engineering, and Medicine. 2019. Management and Use of Data for Transportation Performance Management: Guide for Practitioners. Washington, DC: The National Academies Press. doi: 10.17226/25462.
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Suggested Citation:"Cases." National Academies of Sciences, Engineering, and Medicine. 2019. Management and Use of Data for Transportation Performance Management: Guide for Practitioners. Washington, DC: The National Academies Press. doi: 10.17226/25462.
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Suggested Citation:"Cases." National Academies of Sciences, Engineering, and Medicine. 2019. Management and Use of Data for Transportation Performance Management: Guide for Practitioners. Washington, DC: The National Academies Press. doi: 10.17226/25462.
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Suggested Citation:"Cases." National Academies of Sciences, Engineering, and Medicine. 2019. Management and Use of Data for Transportation Performance Management: Guide for Practitioners. Washington, DC: The National Academies Press. doi: 10.17226/25462.
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Suggested Citation:"Cases." National Academies of Sciences, Engineering, and Medicine. 2019. Management and Use of Data for Transportation Performance Management: Guide for Practitioners. Washington, DC: The National Academies Press. doi: 10.17226/25462.
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Suggested Citation:"Cases." National Academies of Sciences, Engineering, and Medicine. 2019. Management and Use of Data for Transportation Performance Management: Guide for Practitioners. Washington, DC: The National Academies Press. doi: 10.17226/25462.
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Suggested Citation:"Cases." National Academies of Sciences, Engineering, and Medicine. 2019. Management and Use of Data for Transportation Performance Management: Guide for Practitioners. Washington, DC: The National Academies Press. doi: 10.17226/25462.
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Suggested Citation:"Cases." National Academies of Sciences, Engineering, and Medicine. 2019. Management and Use of Data for Transportation Performance Management: Guide for Practitioners. Washington, DC: The National Academies Press. doi: 10.17226/25462.
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Suggested Citation:"Cases." National Academies of Sciences, Engineering, and Medicine. 2019. Management and Use of Data for Transportation Performance Management: Guide for Practitioners. Washington, DC: The National Academies Press. doi: 10.17226/25462.
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Suggested Citation:"Cases." National Academies of Sciences, Engineering, and Medicine. 2019. Management and Use of Data for Transportation Performance Management: Guide for Practitioners. Washington, DC: The National Academies Press. doi: 10.17226/25462.
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Suggested Citation:"Cases." National Academies of Sciences, Engineering, and Medicine. 2019. Management and Use of Data for Transportation Performance Management: Guide for Practitioners. Washington, DC: The National Academies Press. doi: 10.17226/25462.
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Suggested Citation:"Cases." National Academies of Sciences, Engineering, and Medicine. 2019. Management and Use of Data for Transportation Performance Management: Guide for Practitioners. Washington, DC: The National Academies Press. doi: 10.17226/25462.
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Suggested Citation:"Cases." National Academies of Sciences, Engineering, and Medicine. 2019. Management and Use of Data for Transportation Performance Management: Guide for Practitioners. Washington, DC: The National Academies Press. doi: 10.17226/25462.
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Suggested Citation:"Cases." National Academies of Sciences, Engineering, and Medicine. 2019. Management and Use of Data for Transportation Performance Management: Guide for Practitioners. Washington, DC: The National Academies Press. doi: 10.17226/25462.
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Suggested Citation:"Cases." National Academies of Sciences, Engineering, and Medicine. 2019. Management and Use of Data for Transportation Performance Management: Guide for Practitioners. Washington, DC: The National Academies Press. doi: 10.17226/25462.
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Suggested Citation:"Cases." National Academies of Sciences, Engineering, and Medicine. 2019. Management and Use of Data for Transportation Performance Management: Guide for Practitioners. Washington, DC: The National Academies Press. doi: 10.17226/25462.
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Suggested Citation:"Cases." National Academies of Sciences, Engineering, and Medicine. 2019. Management and Use of Data for Transportation Performance Management: Guide for Practitioners. Washington, DC: The National Academies Press. doi: 10.17226/25462.
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Suggested Citation:"Cases." National Academies of Sciences, Engineering, and Medicine. 2019. Management and Use of Data for Transportation Performance Management: Guide for Practitioners. Washington, DC: The National Academies Press. doi: 10.17226/25462.
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Suggested Citation:"Cases." National Academies of Sciences, Engineering, and Medicine. 2019. Management and Use of Data for Transportation Performance Management: Guide for Practitioners. Washington, DC: The National Academies Press. doi: 10.17226/25462.
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Suggested Citation:"Cases." National Academies of Sciences, Engineering, and Medicine. 2019. Management and Use of Data for Transportation Performance Management: Guide for Practitioners. Washington, DC: The National Academies Press. doi: 10.17226/25462.
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Suggested Citation:"Cases." National Academies of Sciences, Engineering, and Medicine. 2019. Management and Use of Data for Transportation Performance Management: Guide for Practitioners. Washington, DC: The National Academies Press. doi: 10.17226/25462.
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Suggested Citation:"Cases." National Academies of Sciences, Engineering, and Medicine. 2019. Management and Use of Data for Transportation Performance Management: Guide for Practitioners. Washington, DC: The National Academies Press. doi: 10.17226/25462.
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Suggested Citation:"Cases." National Academies of Sciences, Engineering, and Medicine. 2019. Management and Use of Data for Transportation Performance Management: Guide for Practitioners. Washington, DC: The National Academies Press. doi: 10.17226/25462.
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Suggested Citation:"Cases." National Academies of Sciences, Engineering, and Medicine. 2019. Management and Use of Data for Transportation Performance Management: Guide for Practitioners. Washington, DC: The National Academies Press. doi: 10.17226/25462.
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Suggested Citation:"Cases." National Academies of Sciences, Engineering, and Medicine. 2019. Management and Use of Data for Transportation Performance Management: Guide for Practitioners. Washington, DC: The National Academies Press. doi: 10.17226/25462.
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Suggested Citation:"Cases." National Academies of Sciences, Engineering, and Medicine. 2019. Management and Use of Data for Transportation Performance Management: Guide for Practitioners. Washington, DC: The National Academies Press. doi: 10.17226/25462.
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Suggested Citation:"Cases." National Academies of Sciences, Engineering, and Medicine. 2019. Management and Use of Data for Transportation Performance Management: Guide for Practitioners. Washington, DC: The National Academies Press. doi: 10.17226/25462.
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Suggested Citation:"Cases." National Academies of Sciences, Engineering, and Medicine. 2019. Management and Use of Data for Transportation Performance Management: Guide for Practitioners. Washington, DC: The National Academies Press. doi: 10.17226/25462.
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Suggested Citation:"Cases." National Academies of Sciences, Engineering, and Medicine. 2019. Management and Use of Data for Transportation Performance Management: Guide for Practitioners. Washington, DC: The National Academies Press. doi: 10.17226/25462.
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Suggested Citation:"Cases." National Academies of Sciences, Engineering, and Medicine. 2019. Management and Use of Data for Transportation Performance Management: Guide for Practitioners. Washington, DC: The National Academies Press. doi: 10.17226/25462.
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Suggested Citation:"Cases." National Academies of Sciences, Engineering, and Medicine. 2019. Management and Use of Data for Transportation Performance Management: Guide for Practitioners. Washington, DC: The National Academies Press. doi: 10.17226/25462.
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Suggested Citation:"Cases." National Academies of Sciences, Engineering, and Medicine. 2019. Management and Use of Data for Transportation Performance Management: Guide for Practitioners. Washington, DC: The National Academies Press. doi: 10.17226/25462.
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Suggested Citation:"Cases." National Academies of Sciences, Engineering, and Medicine. 2019. Management and Use of Data for Transportation Performance Management: Guide for Practitioners. Washington, DC: The National Academies Press. doi: 10.17226/25462.
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Suggested Citation:"Cases." National Academies of Sciences, Engineering, and Medicine. 2019. Management and Use of Data for Transportation Performance Management: Guide for Practitioners. Washington, DC: The National Academies Press. doi: 10.17226/25462.
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Suggested Citation:"Cases." National Academies of Sciences, Engineering, and Medicine. 2019. Management and Use of Data for Transportation Performance Management: Guide for Practitioners. Washington, DC: The National Academies Press. doi: 10.17226/25462.
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Suggested Citation:"Cases." National Academies of Sciences, Engineering, and Medicine. 2019. Management and Use of Data for Transportation Performance Management: Guide for Practitioners. Washington, DC: The National Academies Press. doi: 10.17226/25462.
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Suggested Citation:"Cases." National Academies of Sciences, Engineering, and Medicine. 2019. Management and Use of Data for Transportation Performance Management: Guide for Practitioners. Washington, DC: The National Academies Press. doi: 10.17226/25462.
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Below is the uncorrected machine-read text of this chapter, intended to provide our own search engines and external engines with highly rich, chapter-representative searchable text of each book. Because it is UNCORRECTED material, please consider the following text as a useful but insufficient proxy for the authoritative book pages.

Cases

Introduction • Foundation • Reporting • Insight • Cases Case A Arizona DOT Long-Range Plan Investment Trade-offs Like many states, Arizona has a gap between the transportation needs of its growing population and funding available to pay for those needs. Consequently, Arizona DOT (ADOT) and its stakeholders must make difficult trade-offs across transportation investment priorities. This case shows how ADOT utilized data to make these trade-offs as part of its What Moves You Arizona 2040 Long-Range Statewide Transportation Plan update (LRSTP or 2040 Plan). The 2040 Plan process used decision science techniques in combination with stakeholder engagement to quantify the likely performance outcomes of several investment scenarios designed by ADOT staff and stakeholders. This process guided the selection of a recommended investment choice (RIC) for the plan, which has provided the starting point to invest approximately a billion dollars a year in alignment with consensus transportation priorities of Arizona’s citizens and businesses. Cases 65

Introduction • Foundation • Reporting • Insight • Cases Overview Arizona’s LRSTP four-step planning process relied equally on data, public engagement, and use of multi-objective decision analysis (MODA) software. First, it mined rich engineering data about capital and operating needs and revenues. Second, it used these data within a public engagement process to identify stakeholder goals and priorities. Third, it used the MODA software to enable stakeholders to explore performance projections of Arizona’s future transportation safety, congestion, and infrastructure condition under various alternate transportation futures propelled by divergent investment strategies. Finally, it used stakeholder input to inform a recommended investment strategy. Foundation: Specify & Define Data Estimates of capital and operating needs by transportation investment category. Arizona Department of Transportation oversees a statewide system of major highways and supports transit, rail, aviation, and non-motorized transportation facilities around the state. The 2040 Plan used established data sources and modeling tools such as FHWA’s Highway Performance Monitoring System (HPMS), National Bridge Investment Analysis System (NBIAS), and Highway Economic Requirements System—State Version (HERS-ST) to document $89.5 billion in baseline 25-year needs for all of the state’s major transportation investment categories: • Preservation investment needs to maintain pavement and bridges in good repair; • Modernization investment needs for upgrades like safety improvements and intelligent transportation systems; • Expansion investment needs for added lanes, new roadway alignments, or interchanges; • Operations and maintenance investment needs for routine work, like patching potholes, fixing guardrails, mowing, and snow removal; and • Non-highway investment needs for transit, rail, non-motorized, and aviation modes. These estimates were derived from segment-by-segment data and analysis of all engineering work needed to achieve and maintain an acceptable level of performance throughout the state for each major investment category. In combination with funding information, they Multi-Objective Decision Analysis (MODA) Multi-objective decision analysis (MODA) is a tool for resolving resource allocation problems where choices involve trade-offs among competing objectives that feature sacrifice of one objective for the sake of another. MODA uses data about stakeholders’ preferences and decision outcomes to guide selection of optimum choices. Initially, the relative importance ascribed by stakeholders to different choices is scored using weighting techniques. Subsequently, outcomes of different choices are evaluated in terms of their relative alignment with stakeholders’ priorities to arrive at an optimum solution. Management and Use of Data for Transportation Performance Management: Guide for Practitioners 66

Introduction • Foundation • Reporting • Insight • Cases provided a baseline indicator of the potential investment trade-offs Arizona faces: highway revenue forecasts suggested that $23 billion would be available over the 2040 Plan’s 25-year time horizon compared to $53 billion in highway needs. Statewide transportation system goals and performance outcome measures. Building on information developed about the state’s transportation needs, ADOT—with extensive input from its stakeholders—set broad goals for the state’s transportation system in 2040 and measurable yardsticks for gauging progress toward them: • Improve mobility, reliability, and accessibility. Implement critical/cost-effective investments to improve access to multimodal transportation and optimize mobility and reliability for passengers and freight. (Measures: congestion, speed, travel delay) • Preserve and maintain the system. Maintain, preserve, and extend the service life of existing and future state transportation system infrastructure. (Measures: pavement and bridge deficiencies; maintenance spending) • Enhance safety. Continue to improve and advocate for transportation system safety for all modes. (Measures: fatalities and serious injuries) Stakeholders’ relative priorities for these three transportation system- related goals helped inform the build-out of possible scenarios for investment across major investment categories like preservation, modernization, and expansion. The measures associated with each goal provided the basis for comparison of each scenario’s system performance outcomes and its consequences for spending trade-offs. Insight: Analyze & Use, Present & Communicate Data Stakeholders’ priorities and scenarios for Arizona’s future transportation performance. The 2040 Plan broke new ground in its methods for aligning Arizona stakeholders’ priorities with competing 25- year transportation needs. ADOT innovated by using a MODA software platform with intuitive interface elements like slider-bars, dashboards, and data visualizations that helped stakeholders see the consequences of alternate funding choices on performance outcomes. Importantly, the software does not pick the “best” investment strategy; rather it allows users to compare strategies and choose which one they like best based on data about performance outcomes. Both stakeholders and staff at ADOT were enthusiastic about the software’s ability to show Cases 67

Introduction • Foundation • Reporting • Insight • Cases performance trade-offs in real time as users moved funding from one investment area to another. The MODA software was customized with Arizona-specific “performance curve” algorithms for each investment need category, including preservation, modernization, and capacity.1 The curves show how a rise or fall in spending changes performance outcomes. ADOT invested considerable effort in deriving each curve by processing raw transportation data that depicted performance outcomes at different spending levels to establish generalized rules. In the preservation category, for example, pavement and bridge curve algorithms were built from outputs of infrastructure management system data. In the capacity category, the performance curve algorithm was derived from projected changes in delay and reliability as sets of proposed projects are built. Through a series of workshops and webinars, ADOT used the decision- support software to develop several distinct “alternative investment choice” scenarios that showed how different spending strategies might affect the state’s transportation system performance: • “Current Plan” Investment Scenario. ADOT projected future performance based on continuation of current capital spending without any changes. • “Agency Plan” Investment Scenario. ADOT used an interactive process to discuss ADOT staff and outside stakeholders’ preferences for investment (gathered via a webinar and an in-person workshop). Through this process, a consensus based on projections of future performance relative to a baseline was developed about allocation of resources. • Public Investment Scenario. ADOT used MetroQuest, a web-based public engagement software platform, to gather general public input on highway investment priorities. Nearly 6,000 individuals provided their opinions about transportation priorities and potential trade-offs. Arizona’s recommended investment choice (RIC). At the culmination of ADOT’s 2040 Plan process, the three scenarios developed during the planning process informed the 2040 Plan’s RIC (as shown in 1Because annual operations spending levels are determined independently by the Arizona legislature and ADOT does not have the ability to allocate these funds to highway capital spending, Operation and Maintenance needs were excluded from the scenarios. Management and Use of Data for Transportation Performance Management: Guide for Practitioners 68

Introduction • Foundation • Reporting • Insight • Cases Figure 4) by providing a sense of different stakeholders’ priorities for use of ADOT resources and the projected performance outcomes associated with them. The combination of data, tools for projecting future needs, public engagement, and MODA in the scenario development process helped build support for tough trade-off decisions about limiting expansion spending to avoid a future decline in system preservation performance. Figure 4. Annual funding levels—Arizona Long-Range Plan recommended investment choice (RIC). Development of RIC using scenarios and MODA tools engaged stakeholders in Arizona in new ways and educated them about the importance of thinking about priority weighting, measures of performance, the necessity of trade-offs, and target setting. Based on the successful application of these techniques for the 2040 Plan, a similar MODA-based process will likely be used for translating What Moves You Arizona 2040 into ADOT’s programming process. Success Factors Using a MODA-based scenario approach for long-range planning helped ADOT achieve a more informed recommendation for how to allocate scarce transportation funding in the future. Success factors included: Cases 69

Introduction • Foundation • Reporting • Insight • Cases • Taking advantage of available data sets and tools. HPMS, NBIAS, HERS-ST, and asset management systems were used to create projections of future performance under varying investment levels. • Incremental approach. ADOT recognized that advancement in analysis capabilities would be an incremental process. The agency was able to establish outcome-oriented performance curves for certain investment areas (e.g., expansion, preservation). For investment areas where ADOT could not develop outcome-oriented curves, it based the curves on the percentage of identified needs met at a given allocation level (e.g., safety, technology). While the methodologies for the curves differed, the resulting analysis was still valuable in informing investment decisions. • Interactive analysis. ADOT provided interactive tools that enabled stakeholders to explore implications of different investment strategies. Stakeholders and staff alike benefitted from the ability to see information about performance trade-offs in real time as they collaborated to explore shifts in funding from one investment area to another. • Opening up the black box. The interactive process educated stakeholders about the impacts of various model parameters and assumptions; for example, in the ADOT MODA tool, performance curves, performance thresholds, and criteria weights were key drivers of the results. • A data-informed decision-making philosophy. ADOT recognized and acknowledged that decisions are informed—not made —by the data and analysis results. Communicating this philosophy alleviated stakeholder concerns that the analysis did not consider other non-quantifiable factors. Challenges & Lessons Communicating with stakeholders. Key challenges and lessons learned were related to communicating technical information to stakeholders in a clear and succinct manner and making sure they understood definitions, implications of assumptions, and limitations of the analysis: • During the 2040 Plan development, some stakeholders were confused about the difference between “preservation” and routine “operations and maintenance.” This may have led to preservation being underweighted. • Many participants struggled with the pairwise comparison, which asks a respondent to identify the relative priority between two different Management and Use of Data for Transportation Performance Management: Guide for Practitioners 70

Introduction • Foundation • Reporting • Insight • Cases investment options and served as the basis for criteria weighting. This was either because they were not comfortable with some of the comparisons (e.g., how can you compare preservation and safety), or because they simply did not understand its purpose. • The MODA approach did not enable users to consider synergies in spending, such as the benefits to safety or mobility that might come from increased preservation spending. Integrating consideration of investment synergies across performance areas is an area identified for future improvement. For more information... • Arizona Long Range Statewide Transportation Plan https://www.azdot.gov/planning/ transporation-programs/state- long-range-transportation-plan • Arizona DOT Point of Contact: Statewide Planning Manager Cases 71

Introduction • Foundation • Reporting • Insight • Cases Case B Caltrans State Highway System Management Plan Caltrans developed an integrated State Highway System Management Plan (SHSMP) that summarizes conditions of existing transportation assets, communicates funding needs, and details projected funding levels. The plan addresses an extensive array of asset types, two different major investment programs, and forty different subprograms. This case illustrates how data across different sources at different levels of completeness can be integrated and put on a comparable footing for analysis and presentation. Management and Use of Data for Transportation Performance Management: Guide for Practitioners 72

Introduction • Foundation • Reporting • Insight • Cases Overview At Caltrans, capital projects to preserve, rehabilitate, or replace existing transportation assets are included in the State Highway Operations and Protection Program (SHOPP). Caltrans manages a separate Highway Maintenance (HM) program for smaller maintenance projects. Together the annual budgets for these programs are projected to total over $4 billion per year over the next 10 years. The California Streets and Highways Code requires Caltrans to prepare periodic updates to its SHOPP and HM programs. Historically these updates were made separately, in some cases drawing upon different data sources. In 2017, Caltrans developed a new, integrated SHSMP that incorporates state requirements for preparation of both a ten-year plan for the SHOPP and five-year HM plan. The SHSMP (shown in Figure 5) includes a Needs Assessment and Investment Plan to help guide the management of the state highway system and related infrastructure. The plan covers thirty-four different SHOPP subprograms and six maintenance subprograms. These subprograms address physical assets including but not limited to • pavement • bridges • drainage systems • lighting • signage • guardrail • transportation management systems • water/wastewater treatment • rest areas • facilities Foundation: Specify & Define, Obtain Data Data for the SHSMP were obtained from several different sources. For major assets (pavement and bridge), Caltrans has well-defined management systems and analytical processes. For selected assets (e.g., traffic management systems), Caltrans has established asset inventory databases but no formal analytical processes. For other assets (e.g., drainage systems), Caltrans has partial inventory information and relies on a sample of the inventory together with estimates of the extent of missing data to determine the overall size of the inventory. Cases 73

Introduction • Foundation • Reporting • Insight • Cases Figure 5. Caltrans SHSMP. (Courtesy of California Department of Transportation) Reporting: Store Data To facilitate development of the 2017 SHSMP, Caltrans requested data from asset data owners in whatever format was available and then developed a standardized means for representing data on the asset inventory, its condition, and investment needs. This standard representation was implemented in a spreadsheet, with separate sheets for each investment sub-program. Figure 6, reproduced from the SHSMP, illustrates the format used for summarizing and reporting the data. The summary view includes 13 distinct sections, including the asset inventory, projected inventory, predicted investment level, and total investment need. Management and Use of Data for Transportation Performance Management: Guide for Practitioners 74

Introduction • Foundation • Reporting • Insight • Cases Figure 6. A SHSMP summary sheet with sections labeled. (Courtesy of California Department of Transportation) A. Current inventory of physical assets B. Projected future inventory of physical assets C. Annual deterioration rates used to calculate projected condition D. Current condition of physical assets E. Projected future condition of physical assets (do-nothing scenario) F. Asset quantities from work in the pipeline G. Performance targets (not constrained) H. Performance gaps I. Average unit costs for repair and associated support ratio J. Dollar value of unfunded future commitments K. Dollar value necessary to close the performance gap L. Total need to achieve the performance target M. District level breakdown of inventory, gaps and needs Cases 75

Introduction • Foundation • Reporting • Insight • Cases Insight: Analyze & Use, Present & Communicate Data A critical step in developing the integrated SHSMP was establishing a common approach to characterizing asset conditions and investment needs across investment areas and asset types. This was needed both to help communicate the plan and to standardize the analysis approach to allow for meaningful comparisons across the different areas. A complicating factor was that in some cases, such as for pavement, Caltrans had already implemented a sophisticated approach to predicting conditions and establishing needs, while in other cases such an approach had not yet been implemented. Caltrans’ approach to this step was to define criteria for good, fair, and poor condition for each asset/investment type and then characterize existing conditions in these terms. In some cases, such as where an asset’s condition is assessed strictly in terms of age (e.g., for traffic management systems), the approach was further simplified to include good and poor conditions only. Caltrans staff then established, for each asset/investment category, the likelihood of deterioration from good condition to fair condition (or poor, if fair is not defined) and from fair condition to poor condition (if fair condition is defined). For cases where a formalized management system had been established, these values were calculated based on model runs from the existing system. Alternatively, staff established the values based on supplemental analysis and/or expert judgment. Figure 7 shows an example of this calculation for bridges. Here 75 percent of the existing inventory is in good condition, and 0.45 percent is expected to drop to fair condition annually. An additional 22 percent is in fair condition, and 0.75 percent of these assets are expected to drop to poor condition annually. Management and Use of Data for Transportation Performance Management: Guide for Practitioners 76

Introduction • Foundation • Reporting • Insight • Cases Figure 7. Example representation of good/fair/poor condition and predicted deterioration. (Courtesy of California Department of Transportation) With this representation, future conditions at the end of a 10-year period can be approximated based on initial conditions, expected budget, and the average treatment cost in fair and poor condition. Caltrans performed this calculation for each asset/investment area to predict future conditions and needs. The end result was the development of an integrated plan using a common, easily communicated representation to establish and illustrate expected investment levels over a wide range of assets and investment areas in two different funding programs. Cases 77

Introduction • Foundation • Reporting • Insight • Cases Success Factors • Alignment between analysis and available data. The investment categories in Caltrans’ SHOPP and maintenance programs are well defined, and in many cases they are aligned with specific asset classes. This simplified the process of determining what data were needed to develop the SHSMP and help address the organization of data in the plan. • Common performance measures across assets. Development of a common approach for analyzing and summarizing asset/investment data was a critical step in preparing an integrated plan. One key insight was that using a simplified approach to presenting data (e.g., good/fair/poor asset conditions) allowed for an effective means to summarize data in cases where a more complicated approach was used for analysis while also providing a basic analytical approach in cases where only summary data were available on the asset inventory and its condition. • Streamlined presentation. The resulting SHSMP uses standardized graphics for communicating conditions and deterioration rates. Details on each asset/investment area are included in an appendix to the document. Use of a standard approach helped simplify the presentation of the materials and streamlined document preparation. Challenges & Lessons Lack of integrated data system. Caltrans did not, as of the development of the 2017 SHSMP, have an integrated system for collecting and managing the data used to prepare the document. Caltrans is exploring the feasibility of implementing an integrated asset management system to help support development of the SHSMP and other related documents and plans in the future. For more information... • Caltrans 2017 State Highway System Management Plan http://www.dot.ca.gov/assetmg mt/documents/SHSMP.pdf • Caltrans Point of Contact: State Transportation Asset Engineer Management and Use of Data for Transportation Performance Management: Guide for Practitioners 78

Introduction • Foundation • Reporting • Insight • Cases Case C Florida DOT Transportation Data Portal Open data portals have been developed at the federal, state, and local levels. The U.S. Federal Open Data Portal, deployed in 2009, provides access to more than 300,000 data sets on data.gov. U.S. DOT has created their own open data portals, including its.dot.gov/data/. States are also developing their own open data portals. This case describes one state’s approach to creating a data portal. Florida DOT’s Transportation Data Portal was developed to help FDOT, the public, contractors, and other 3rd-parties locate and utilize data for internal and external projects. FDOT’s portal is “a platform for locating data related to the core mission of the Florida Department of Transportation.” Cases 79

Introduction • Foundation • Reporting • Insight • Cases Overview maps; develop new web/mobile applications, and more.” FDOT’s available transportation data includes everything from GIS shapefiles describing transportation facilities, aerial photography, documents, manuals, real- time and historical traffic counts, summary statistics, interactive web applications, assets, software, and much more. They created a data portal that meets the needs of multiple stakeholders, including internal employees, those doing business with FDOT, and the public. Reporting: Share Data FDOT took a multi-faceted approach to reporting and sharing data with their stakeholders. FDOT is a relatively decentralized agency with seven large districts and many different business units. Each business unit had already been making select data sets available on disparate websites. The Central Office eventually stepped in and decided to consolidate their transportation data, reports, and other resources together in a single site. Some business units provided their data directly to this new site while others simply had their existing websites linked from this central data portal. While this makes it slightly more difficult for users to locate everything they might need, the DOT is clearly making great strides toward open government and open data. Insight: Present & Communicate Data FDOT’s data portal (shown in Figure 8) is more than just a listing of live and archived data sets. They also have applications, reports, and other materials that provide insights, present the data in a digestible format, and otherwise allow for interactive exploration. FDOT resource to allow people “to explore and download open geospatial data; analyze and combine open data sets using ’s goal was to provide a Management and Use of Data for Transportation Performance Management: Guide for Practitioners 80

Introduction • Foundation • Reporting • Insight • Cases Figure 8. FDOT's transportation data portal provides links to many FDOT data resources across different business units. Figure 9 shows a user exploring aerial photography for a part of the state. Users can search for photos by year, specific date, location, format, etc. Results for small queries are shown immediately, while larger queries may require additional time or retrieval options that go beyond online access. Cases 81

Introduction • Foundation • Reporting • Insight • Cases Figure 9. A screenshot of Florida's Aerial Photo Lookup System. Figure 10 shows another interactive application on Florida’s transportation data portal that enables a user to explore traffic monitoring sites, counts, districts, and summary statistics. There are many additional summary reports and analyses that can be explored with other applications—even apps dedicated to exploring data about the location of wildflowers, meadows, and other beautification projects on or adjacent to Florida roadways. Management and Use of Data for Transportation Performance Management: Guide for Practitioners 82

Introduction • Foundation • Reporting • Insight • Cases Figure 10. In addition to raw data sets, FDOT makes many data exploration websites available to the public. The image above is of FDOT's continuous count stations. Success Factors • Legislative action: In 2011, Florida’s governor issued Executive Order 11-03 establishing the Office of Open Government. This order required the state to establish and maintain a website providing ready access to accountability information and required each Florida agency to establish an Open Government contact. • Leading by example: Several business units within the DOT had already started to post important data sets online. This was done for several reasons, including trying to proactively keep consultants and the public from flooding FDOT phone lines and inboxes with data requests, thus freeing up employees to conduct other business. Certain FDOT business units also believed that providing data to the public could potentially spur innovative solutions to FDOT’s growing transportation problems. The business units that were already successfully sharing their data with the public could tout their success and show the positive ROI to other business units that had not yet begun to share data. Cases 83

Introduction • Foundation • Reporting • Insight • Cases Challenges & Lessons Agency buy-in. Not every business unit initially thought that providing all of their data online was a good idea. Providing data to the public can open up the business unit (and the managers of the data) to increased scrutiny. Some data managers felt they had little to gain and much to lose. Others feared that making their data more easily accessible would lead to a devaluation of their own job. They were no longer the gate-keepers of the data, and some thought that was a job security concern. The lesson learned is to anticipate these concerns and address them head-on. Organization and presentation. FDOT has tried very hard to make the bulk of their data available online. This includes a wide variety of data sets, including maps, asset inventory, and traffic counts. Because of the diversity in data offerings, organizing the data in a way that makes it easily discoverable has been a challenge. Also, some business units were already making their data available through disparate web pages. Joining all of these web pages together into a truly centralized one-stop shop is an ongoing challenge. For more information... • Florida Office of Open Government https://www.flgov.com/open_go vernment/ • FDOT Transportation Data Portal http://www.fdot.gov/agencyreso urces/mapsanddata.shtm • FDOT Open Data Hub https://gis- fdot.opendata.arcgis.com/ • U.S. DOT’s ITS JPO Data Portal https://www.its.dot.gov/data/ Management and Use of Data for Transportation Performance Management: Guide for Practitioners 84

Introduction • Foundation • Reporting • Insight • Cases Case D I-95 Corridor Coalition Probe Vehicle Data Procurement In 2007, the I-95 Corridor Coalition issued an innovative, multistate request for proposals (RFP) for purchase of private-sector, probe-based travel time. The individuals behind this procurement had several goals in mind: 1. Demonstrate the value of 3rd-party probe-based speed data to states, 2. Make it easier for states to procure data uniformly, and 3. Remedy private-sector acceptable use issues that had plagued agencies in prior contracts. As a result of this RFP and follow-up efforts, the I-95 Corridor Coalition established one of the most liberal and flexible data use agreements that has become the “gold standard” for agencies and consortiums across the country for over a decade. This case illustrates how, while procurement mechanisms and data products have changed over the last decade, the underlying foundation of data ownership, acceptable use, quality expectations, etc., has not. Cases 85

Introduction • Foundation • Reporting • Insight • Cases Overview Most agencies write RFPs for data from the private sector in a vacuum. They may forget to talk to other stakeholders in their own agency— procuring the data for a single use only. They may not think strategically about future applications of the data. They may not seek out “lessons learned” from other DOTs who have procured similar data. And worst of all, they may neglect to specify acceptable use terms at all—leaving it completely up to the data provider. For example, many agencies deployed and owned Closed Caption Television (CCTV) infrastructure, but contracted with 3rd parties to stream those videos internally and with their customers (television stations, agency traveler information sites, etc.). In those contracts, agencies ended up having to pay to view their own video or share it with others. The 3rd party monetized an asset that wasnottheirs by taking advantage of agencies’ inexperience in negotiation of acceptable use agreements. Similarly, some 3rd-party speed sensor data providers negotiated the installation of private-sector sensors on the public right-of-way in exchange for allowing the agency to view the data coming from those sensors. While at a glance that seems to be a reasonable partnership, that data exchange came with many strings attached, effectively preventing agencies from doing useful things with the data (like posting travel times on variable message signs or on the web) unless the agency paid significant additional fees. In effect, agencies traded valuable right-of-way for a data set of very limited value due to acceptable use agreements. Foundation: Specify & Define, Obtain Data To develop an agency-friendly and private-sector beneficial agreement, the I-95 Corridor Coalition’s I-95 Vehicle Probe Project (Figure 11) took a bold approach of defining acceptable use terms that incorporated multistate sharing, perpetual use, data quality standards, unrestricted non- commercial use, and other innovative clauses. DOTs have been procuring traffic data and information services from the private sector for many years. Data use agreements—documents that state what can and cannot be done with private-sector data—are a standard component of these public–private data procurements. Unfortunately, many agencies end up with data use agreements that heavily favor the private sector and severely limit the agency’s ability to utilize the data in a way that benefits everyone. Management and Use of Data for Transportation Performance Management: Guide for Practitioners 86

Introduction • Foundation • Reporting • Insight • Cases Figure 11. I-95 Corridor Coalition’s Vehicle Probe Project. Agencies exchanged best and worst practices from their previous procurements and contracts and generated a list of necessary components for this agreement. Academics and consultants provided insights on the future of planning and operations to cover a wide range of situations. No stakeholder was left out of the discussion, and the Coalition did not shy away from demanding certain terms and conditions. The RFP also encouraged data collection and delivery approaches that did not rely on installation of equipment on public rights-of-way as a means to spur innovation and protect agencies from trading their most valuable asset. Data quality specifications (including latency, accuracy, and availability) ensured that providers were held accountable for the quality of data they provided, regardless of technology used. Financial penalties ensured that providers maintained the quality of their data and processes throughout the length of contract. The University of Maryland was tasked to be an impartial and objective 3rd-party validator of data quality to ensure fair and equitable evaluation. Perhaps most importantly, the I-95 Corridor Coalition (and all state participants who agreed to the terms of the data use agreement) retains the rights to use data in any way it sees fit in perpetuity, with some limitations in sharing with other private entities for commercial purposes. This allowed 3rd party data providers to retain their right to resell data in commercial markets and maintain their competitive edge. This flexibility allowed agencies to use data for a variety of applications ranging from real-time operations and traveler information to planning and project prioritization. The general public also benefitted from an empowered DOT that could suddenly react more quickly, provide better Cases 87

Introduction • Foundation • Reporting • Insight • Cases traveler information, and make data-informed decisions with respect to spending and construction. Because the public sector did a good job setting clear expectations from the beginning, 3rd party data providers saw a marked improvement in their relationship with the public sector. Public-sector participants did not feel restricted or taken advantage of, and more time could be spent on innovating products and building relationships. Agencies and private- sector data providers truly worked together in a mutually beneficial partnership. Success Factors • Collaboration. Because agencies approached this procurement as true collaborators, they were able to leverage their collective knowledge and experience in contracting and procurement to create a strong, public, agency-friendly data use agreement. • Willingness to share. The I-95 Corridor Coalition emphasized the need to share data across jurisdictional borders to support TPM efforts across entire regions. The importance placed on cross- jurisdictional sharing has led to innovation. • Strong champion. The executive leadership of the Coalition was a strong proponent of this project. This leadership helped to push for the collaboration mentioned above and was extremely forceful in demanding some of the more innovative terms and conditions that had heretofore not been requested of data vendors. • Governance. The Coalition established a steering committee made up of leadership from each state. This ensured that no single interest could dominate and kept all of the states actively involved. • Focusing on the end result. This highly successful data use agreement was possible because agencies focused on the end result and allowed the private sector to innovate and meet the needs of agencies in a mutually beneficial manner. Agencies worked together to exchange knowledge and experience and create a common vision for better service to the general public. Management and Use of Data for Transportation Performance Management: Guide for Practitioners 88

Introduction • Foundation • Reporting • Insight • Cases Challenges & Lessons Disparate needs across agencies. Each agency may have different priorities or focus areas depending on their geography—urban versus rural nature—or modes and types of traffic traversing their networks. In the past, this led agencies to think that collaborative solutions would not meet their unique needs. However, the I-95 Corridor Coalition data use agreement showed that despite differences, agencies have many common goals, and with carefully drafted language that is not overly prescriptive, agencies could rely on innovative private-sector solutions to satisfy many disparate missions. Change in established vision of public–private relationships. Through this process, both agencies and private-sector data providers had to adjust to a new kind of relationship. Agencies had to let go of their previous tendencies to view vendors with suspicion, while private-sector providers had to make some concessions in short-term profits in order to establish a positive relationship that would result in overall long-term benefit. Both had to act in true partnership. For more information... • I-95 Corridor Coalition Probe Data RFP http://i95coalition.org/wp- content/uploads/2015/02/RFP_ 82085N_Final_Final.doc?x7056 0 • I-95 Corridor Coalition Vehicle Probe Project Documents http://i95coalition.org/projects/ vehicle-probe-project/ • I-95 Corridor Coalition Data Use Agreement http://www.i95coalition.org/wp- content/uploads/2015/02/VPPII _DUAv9_signed.pdf • Data Use and Application Guide http://i95coalition.org/wp- content/uploads/2015/03/008- 7G_VPP_DATA_USE_Report_ Final_April_2011.pdf?x70560 Cases 89

Introduction • Foundation • Reporting • Insight • Cases Case E Maryland State Highway Administration’s Incident After Action Reviews Many agencies conduct after action reviews (AARs) for major incidents. The purpose of the AAR is to bring together the key operations, personnel, and responders involved in the incident in order to reflect on the successes and failures of their response with the goal of improving future performance. This case highlights the power of data to build a common understanding of what happened and a consensus on needed improvements. Management and Use of Data for Transportation Performance Management: Guide for Practitioners 90

Introduction • Foundation • Reporting • Insight • Cases Overview The most mature transportation operations agencies conduct weekly AARs on all types and categories of events in order to build teams, enhance communication, and continually improve. Some agencies use a manual tracking process—operators and responders fill out paper forms to document AARs. These forms may be completed days or even weeks following an incident and thus rely on foggy, imprecise memories. Then, information from the forms is compiled and used to write reports or discuss the incident in small groups. This manual process can be tedious, leading to less complete data capture and a less-than-enthusiastic group of AAR participants. The Maryland State Highway Administration (MD SHA) has taken a decidedly different approach to conducting AARs that involve automated, electronic data capture and reporting. The result is a more effective AAR that is engaging, easy to conduct, and informative. This approach encourages more frequent AARs and allows the operational response of the agency to be quantified and tracked over time. Foundation: Specify & Define Data MD SHA identified the need for incident data in their business and strategic plans (see “For more information…” at the end of this chapter). They collect data about where incidents are occurring, how many vehicles are involved, which responders (both agencies and vehicle types) have been notified about the incident, when they arrived on the scene, and when they departed. They capture the name of responders, certain tasks that they performed on scene, the road surface conditions, and lane closings/openings over the course of the incident, as well as other operator notes related to information flows, radio communications, and more. These data were deemed necessary not only for real time operations management, but also for MD SHA’s annual performance evaluation and benefit analysis, which helps to justify their operations program and annual budget. Reporting: Store & Manage Data Maryland’s operations platform has been customized and refined since the mid-1980s to collect highly detailed data. The data are collected and stored at MD SHA. Data are archived, time-stamped, and attributed to individual incidents. Cases 91

Introduction • Foundation • Reporting • Insight • Cases Incident data are then combined with data from ITS devices [Dynamic Message Signs (DMS), Closed Caption Television (CCTV) images, volume and speed detectors, signals] and probe-based speed data used to derive queue buildups and congestion levels. Maryland SHA’s data is transmitted in real time to the Regional Integrated Transportation Information System (RITIS) platform that supports reporting and analysis for AARs and performance evaluation. Insight: Analyze & Use, Present & Communicate Data Maryland has developed a series of reporting and visualization tools that are used to help spur discussion and facilitate a dialogue among the responders and operations personnel. They begin by producing a timeline graphic of the response to the incident (Figure 12). Figure 12. Incident timeline. Management and Use of Data for Transportation Performance Management: Guide for Practitioners 92

Introduction • Foundation • Reporting • Insight • Cases This timeline includes every event recorded during the incident, including when responders were notified about the incident, when they arrived, and when they departed. It includes communication logs, DMS activations, queue buildups, photos and videos of the event, and indications of which lanes were blocked over time. Within the timeline, they can expand the list of operator notes and communication logs to see the flow of information between the responders. Figure 13 shows the communication log. Figure 13. Communication log. They also generate animated maps that show queues building (and receding) over time (Figure 14). These maps typically show adjacent roads and arterials so that the agency can better understand how their actions affect others. Animated maps can also be placed side by side to showcase traffic during a particular incident compared to normal traffic conditions. Cases 93

Introduction • Foundation • Reporting • Insight • Cases Figure 14. Animated maps. Side-by-side congestion scan graphics (Figure 15) also show how queues built up and subsided during the day of the event compared to similar days of the week when no incidents occurred. Figure 15. Congestion scan. Management and Use of Data for Transportation Performance Management: Guide for Practitioners 94

Introduction • Foundation • Reporting • Insight • Cases Heat maps then help the agency understand if this location is a high-crash location (Figure 16). Figure 16. Heat maps. Video images at different locations and time points (Figure 17) are available to provide additional documentation of the incident. Figure 17. Example incident documentation supporting AARs. Cases 95

Introduction • Foundation • Reporting • Insight • Cases Finally, user delay cost graphics (Figure 18) help the agency visualize the social financial cost of the delays. For example, the cost of typical user delay on I-495 in Maryland (including the connecting arterials) would be about $150k/weekday. However, during one particularly bad incident (shown above from images captured by MD SHA in RITIS), the cost of user delay skyrocketed to over $1.2M. This conservative estimate did not account for delays in the opposite direction of travel, excess fuel consumption, emissions, or the cost of equipment damage. Figure 18. User delay cost graphics. In less than an hour, MD SHA is able to create powerful slide decks from the above data reports. These slide decks are then sent out to the responders who participate in AAR meetings in-person or remotely. During AAR meetings, the slide decks documenting the incident are used to facilitate a discussion. Each agency comments on what the data is telling them, including how they responded, how they communicated, etc. This process helps the responders determine what they did well and what they could have done better. The RITIS reports validate the discussion and lend credibility to the responders’ accounts of what happened. These reports also help the responders to understand the societal impacts of their actions (or inactions), which can help drive policy. Two examples illustrate how insights from the reports led to changes in operations policies: • In one AAR meeting, MD SHA was able to make the case that the state police needed to adjust how they manage their tow list to ensure that towing companies have the necessary equipment for heavy duty operations. This recommendation was based on clear data from the incident timeline that showed multiple tow dispatches and long arrival times. • Firefighters tended to close many lanes (or all lanes) of the roadway during their response, resulting in high delay costs. However, information captured in the timeline tool, queue graphics, user delay cost graphics, and additional reports on secondary incidents have Management and Use of Data for Transportation Performance Management: Guide for Practitioners 96

Introduction • Foundation • Reporting • Insight • Cases helped convince the responder community to change its policies for blocking lanes and move more toward a stronger quick-clearance mentality. Success Factors • Specifying and obtaining the right data. The performance reports described above were only possible because MD SHA spent considerable time over the last couple of decades defining data needs and dedicating funding and operator training to ensure that all necessary data is collected. • Analysis tools tailored to decision maker needs. Analysis tools provide quick access to data and show the benefits of quick-clearance practices and the value of transportation systems management and operations programs. • Effective visualizations. The reports and visualization provide the agency ammunition for requests for funding, positions, and equipment. MD SHA’s early investments in data and analytics are paying off. • Commitment to data-driven decision making. In the past, AARs were more about “war stories” than data analysis. As a result, they reinforced or justified existing behaviors rather than provide an opportunity for new insights. Now, however, data, tools, and processes are in place to conduct regular AARs, and those tools provide data-backed conclusions. The agency can be more confident in its decision making, and the tools assist MD SHA in making the case to external (and internal) partners about improving current practices. Over time, the agency will be able to analyze trends along individual corridors and quantify the effects of actions taken based on the AARs. Challenges & Lessons Making the case for investing in data. Operators already face demanding jobs, and asking them to collect more data was an uphill battle. Early education and advocacy were needed at all levels. Senior management had to be convinced that the extra workload would be worth the effort. Funding and implementation. Even after the agency made the decision to collect more data, it took a great deal of time to raise funds and enhance systems to add new data fields, train staff, and see a return on the investment. It is important to keep in mind that implementation takes time and to set appropriate expectations. For more information... • The Maryland DOT CHART Strategic Planning website can be found here: https://chart.maryland.gov//read ingroom/RR_StrategicPlanning. asp • A video of the Statewide Operations Center (SOC) Operations Manager for MDOT discussing their AAR reporting procedures using a fatal incident example can be found here: https://vimeo.com/207690734# t=567s • Maryland DOT Point of Contact: State Operations Center Manager • RITIS Point of Contact: University of Maryland’s CATT Lab Director Cases 97

Introduction • Foundation • Reporting • Insight • Cases Case F MATOC Regional Operations Evaluation Following experiences from the 9/11 attacks and other major incidents, transportation officials from Maryland, Virginia, the District of Columbia, and the Washington Metropolitan Area Transit Authority (WMATA) committed to share and coordinate their transportation systems’ conditions and information management during regional incidents. They formed the Metropolitan Area Transportation Operations Coordination (MATOC) Program. MATOC is staffed like a small traffic operations center. The staff integrate system technologies, improve procedures and planning, and provide accurate and timely transportation information to the public. This enables participating agencies to work together to make travel smoother and safer. This case describes how the MATOC program has been able to use its shared pool of operational data to support a quantitative analysis of benefits and costs in support of a regional coordination program. Management and Use of Data for Transportation Performance Management: Guide for Practitioners 98

Introduction • Foundation • Reporting • Insight • Cases Reporting: Store & Manage, Share Data Through MATOC, transportation agencies from Maryland, Virginia, and the District of Columbia realized that they could benefit from coordinating not only during significant events, but also to support their day-to-day operational needs. MATOC integrates disparate agency systems and policies to support real-time coordination and information dissemination. MATOC uses the RITIS to provide a common platform for data sharing and analysis. As the program grew, the number of jurisdictions contributing data sets grew as well. Insight: Analyze & Use Data Metropolitan Washington Council of Governments (MWCOG) engaged an independent contractor to perform a benefit-cost analysis of the program. As illustrated in Figure 19, the analysis evaluated the response to three representative incidents in the region where MATOC was involved. These incidents were compared to calibrated model incidents where MATOC actions were not present—resulting in estimated costs that were annualized based on historical data. Figure 19. Link-based speed visualization. The analysis found that an average of 224 police-reported vehicle-related accidents occur per day across the region. MATOC helps to manage approximately ninety regionally significant incidents per month. Annual benefits of direct MATOC action were estimated at $12.9 million in mobility savings, which includes a greenhouse gas savings of more than $500,000. Cases 99

Introduction • Foundation • Reporting • Insight • Cases This is a conservative estimate and does not include the costs of secondary incident reduction. The positive ROI stemmed primarily from enhanced real-time data sharing among agencies. This allowed agencies to more quickly become aware of incidents, respond, clear the incident, and alert travelers, and to develop standard operating procedures that account for impacts of regional and cross-jurisdictional events. Using the RITIS as a data sharing, warehousing, visualization, and dissemination platform, agencies had easy access to regional performance measures data that included detailed incident and incident response information, as well as flow information from traditional sensors and probe vehicles. A sample RITIS incident information display is shown in Figure 20. The byproducts of this data sharing and collaboration were improvement of each agency’s data (since others were relying on it) and the ability to provide better and more relevant traveler information. Figure 20. RITIS incident information. Agencies can now analyze their performance across jurisdictions using a system that removes institutional barriers and data challenges. The ability of the region to make a case for a program solely focused on data sharing and collaboration removed any doubt about the value of Management and Use of Data for Transportation Performance Management: Guide for Practitioners 100

Introduction • Foundation • Reporting • Insight • Cases sharing in improving safety and mobility in the region. Presenting a benefit-cost ratio of 10:1 for participating agencies convinced the decision makers to continue to invest in the program and tailor it to the needs of the region as it continues to grow and change. Success Factors • Interagency collaboration. The pooled operational data from multiple agencies enabled the benefit-cost evaluators to look at benefits and costs as they pertain to the entire region, not just a single agency or jurisdiction. • Exposing data as a quality improvement strategy. As data were exposed to a larger audience, a virtuous cycle of quality improvement and data utilization occurred. • Benefit-cost analysis to sustain support. While 9/11 and other major incidents provided the initial impetus for MATOC, conducting a benefit-cost analysis was instrumental to sustaining support for the program. Historical data enabled before-and-after comparisons of incident response that provided the basis for the analysis. Challenges & Lessons Providing a baseline. One of the largest challenges when it comes to evaluating benefits of a program such as MATOC is establishing a baseline performance level. Unlike a capital improvement project that provides a capacity increase, the value of quicker communication or cross- jurisdictional coordination is more difficult to establish. However, the availability of supporting data prior to establishment of MATOC and after the program, and the ability to model incidents, allowed the independent evaluators to calculate tangible benefits of the program. For more information... • MATOC Benefit Cost Analysis White Paper http://www1.mwcog.org/upload s/committee- documents/Yl5ZVlZc20100607 114406.pdf • MATOC Website https://matoc.org/ • RITIS https://ritis.org/ • MATOC Point of Contact: MATOC Facilitator Cases 101

Introduction • Foundation • Reporting • Insight • Cases Case G Creating a Team of Data Experts to Support TPM at the Mid-America Regional Council Mid-America Regional Council (MARC) is the eight- county, bi-state Kansas City Region’s Metropolitan Planning Organization (MPO); it covers four counties in Missouri and four counties in Kansas. Like many MPOs and other transportation agencies, MARC has seen a growing need to gather and analyze data in order to monitor and interpret trends, shape and track progress toward performance goals, and ensure compliance with federal TPM requirements. In response, MARC has developed new staff capabilities and data management practices that give the agency a better ability to handle data analytics and performance management tasks. These new capabilities come in the form of two new “data developers” who dedicate roughly a quarter of their time to TPM compliance. Prior to their hiring, much of MARC’s data-related effort was concentrated on manually obtaining, organizing, and cleaning data. Management and Use of Data for Transportation Performance Management: Guide for Practitioners 102

Introduction • Foundation • Reporting • Insight • Cases Foundation: Specify & Define Data Prioritizing data sets for automation. With the ever-growing importance of data in mind, MARC created a data coordination committee charged with improving agency-wide governance and administration for data management. The committee included liaisons from each department, as shown below in Figure 21. Figure 21. MARC data coordination committee. After inventorying all data housed within MARC, the committee created a “top ten” list of priority data sets for automation. The list was split evenly between transportation- and census-related items. The transportation-related items covered pavement and bridge conditions, safety/crash statistics, National Performance Management Research Data Set (NPMRDS) information, transit route information, and what MARC calls “network attributes” (e.g., functional classification, National Highway System designation). Repurposing staff positions to adapt to changing needs. In order to move forward with its plan to automate data processes, MARC Cases 103

Introduction • Foundation • Reporting • Insight • Cases repurposed two open positions, including a GIS specialist and a demographer, into data developer positions capable of creating and managing systematic workflows for data gathering and organization . While GIS and demographics support needs still exist at MARC, advances in mapping software and web data tools have reduced the amount of time required for these tasks, and MARC concluded it no longer needed full- time employees for those specific functions. Data specialists with public-sector experience. In its search for data developer hires, MARC listed programming and application development skills, along with data analytics and visualization, as base competencies. While not listed as requirements, candidates with planning and public-sector experience were given a strong preference. The data developers MARC hired both had public-sector experience, in addition to the required programming skills; one had worked in the GIS division of a public entity; the other had worked for a transportation engineering firm serving public-sector clients. Both had experience in dealing with various stakeholders and in handling data sets related to public policy issues. Ensuring systems meet the needs of the users. Once hired, the data developers were immediately inserted into the data coordination committee to coordinate their roles with the committee’s goals of enhancing MARC’s data management processes. To ensure the data developers understand how the data they manage is ultimately used, they regularly participate in meetings with transportation planning staff to discuss high-level data needs. More focused, detail-oriented breakout sessions are conducted as well. These meetings serve as a feedback mechanism to compare the data needs of the MPO with the systems created by the developers to address those needs. Reporting: Store & Manage Data Automating data delivery has greatly improved efficiency. Prior to MARC’s hiring of the data developers, accessing data to respond to requests or to meet reporting requirements was time-consuming. Data sets were typically downloaded into Excel files that users then had to manually edit in order to extract the relevant information. Analysts were not necessarily aware of what information was available within the agency and would spend considerable time searching through files and folders in MARC’s servers to see which data sets had already been saved. The developers have since created automated processes to obtain data sets and import them into SQL databases. The databases have front- Management and Use of Data for Transportation Performance Management: Guide for Practitioners 104

Introduction • Foundation • Reporting • Insight • Cases end interfaces that greatly simplify the process of querying them to extract the information MARC needs. This has resulted in MARC being able to dedicate more time toward reviewing and analyzing the data. As information needed to meet federal requirements was given priority, automated processes have been implemented and SQL databases developed for data concerning pavement and bridge conditions, safety measures, and system performance. Insight: Analyze & Use Data Beyond standard reporting: diving deep into the data. While automating data processes was the initial impetus behind MARC’s hiring of the data developers, a valuable byproduct of the efficiency gained in automation was the ability for MARC to review data in ways that it previously had not realized were possible. Of particular note is how MARC now uses the crash data. As a bi-state MPO, MARC receives crash data from two different state DOTs in two different formats. Prior to automation, MARC only took the high-level data points such as fatalities and injuries from the files as further processing would be too time-consuming. With the use of a commercial GIS data integration platform, the data developers were able to combine the raw crash data files from the separate DOTs into one comprehensive dataset that allows MARC to analyze data points it previously was not even aware were included in the files. This includes location data that could now be presented spatially, adding much depth to their analyses. Also, because the process is automated, MARC is now able to refresh this data quarterly, which leads to more timely analyses. The time saved in managing data, combined with new tools to better visualize the data, have contributed to MARC’s ability to conduct special analyses for planning studies and special projects and, in general, to think of new and more inventive ways to analyze the data they have. Whereas before MARC primarily used the data simply to answer the questions required of it, it can now use the data to come up with its own questions and form hypotheses. Success Factors • Focus. Developing a top ten list of data elements/sets for automation helped MARC determine the right skill sets to look for, and also helped the developers focus on high priority projects immediately. • Communication. Having the data developers participate in the performance management team meetings ensures that they have a Cases 105

Introduction • Foundation • Reporting • Insight • Cases good understanding of how the data is to be used so that they can develop the proper systems to acquire and organize the data. • Automation of routine processes. The data developers were able to create automated processes for data collection and subsequently developed databases with user-friendly querying capabilities from which to access the data. • Deployment of data integration and analysis tools. The data developers brought new skill sets and introduced innovative tools that vastly improved MARC’s ability to access and analyze data. While storage and management greatly improved efficiency, it was the introduction of the data integration platform that truly allowed MARC to see the possibilities for more robust data analyses. Challenges & Lessons From “We have two dedicated data developers!” to “We only have two dedicated data developers?” MARC has greatly benefitted from the addition of the data developers and the successful systems they have been able to implement. However, the demand for their services is beginning to outpace their capacity. TPM will always take priority given the federal requirements, but MARC now has to focus on balancing the data developers’ workloads. That entails better defining their roles and responsibilities. Data analysis only gets you so far. MARC cited “institutional inertia” as a challenge, specifically the difficulty in convincing stakeholders to appropriately consider data analyses when making critical decisions. Much of the data MARC is now able to process was not available ten years ago. That lack of availability led people to make decisions that were slightly more political in nature. Unfortunately, that practice carries on through today even though the data is now readily accessible and can more easily be analyzed. A useful committee is often composed of people too busy to sit on it. MARC staff understand the importance of the data coordination committee and its meetings in ensuring the developers have proper guidance, but finding staff bandwidth to keep the meetings going has been a challenge. To address this, MARC is planning on restructuring certain staff roles so that participating in the committee meetings becomes an explicit responsibility. For more information... • MARC data webpage http://www.marc.org/Data- Economy • MARC Point of Contact: MARC Principal Transportation Planner Management and Use of Data for Transportation Performance Management: Guide for Practitioners 106

Introduction • Foundation • Reporting • Insight • Cases Case H New Jersey DOT Project Assessment Reporting Motivated by a desire to be more open in communicating the reason for selecting projects and describing the impacts of projects, the New Jersey Department of Transportation (NJDOT) began to develop the capabilities to analyze individual projects, quantify impacts, and communicate those impacts in a meaningful, reproducible, and easily understandable way. Prior to 2015, this was extremely difficult due to three challenges: 1. The high cost of conducting an analysis, 2. A general lack of before-and-after data, and 3. The difficulty in distilling complex project and mobility data into a digestible format for the public and decision makers. NJDOT adopted a Tactical-Level Asset Management Plan (T-LAMP) for better project and program development and to demonstrate to senior leadership, legislators, and the public effective, transparent funding expenditures with positive performance results. Cases 107

Introduction • Foundation • Reporting • Insight • Cases Foundation: Specify & Define Data The data for this initiative came from INRIX (for speeds and travel times) and NJDOT (for incident, event, and construction location data). All of these data are then provided to the RITIS Platform, where they are fused and integrated. The RITIS Platform is then used for an analysis and for generating the graphics that are inserted into various reporting templates. The performance reports are largely automated within the RITIS Suite. However, the tools still require basic training and domain expertise. The agency was successful at implementing before-and-after studies because they had a couple of dedicated employees (one transportation engineer and one planner) that took the time to attend training sessions, participated in user groups, and collaborated with other agencies who were also using the tools. They also had the support from senior leadership to utilize the new system. Reporting: Store & Manage Data The RITIS Probe Data Analytics Suite includes a series of tools that crunch most of the numbers on behalf of the user. The user, however, must know which analysis needs to be performed and how to select proper parameters in the application and is responsible for interpreting the results and scrutinizing them with additional investigation as needed. The bulk of the work is consolidating all of the results from the analytics and turning them into a story that conveys meaning to the audience. The use of the analytics tools did not require any advanced degrees, and the amount of training needed was minimal; however, the transportation domain expertise of the planner and transportation engineer made interpretation of the results much easier to digest and insert into a narrative that would be more readily digestible by the public. The agency focused primarily on projects that were ranked as the most impactful. The Garden State Parkway is listed as the second worst congested location, as shown in Figure 22. Management and Use of Data for Transportation Performance Management: Guide for Practitioners 108

Introduction • Foundation • Reporting • Insight • Cases Figure 22. Top 10 bottleneck locations—state and authority roadways. Figures 23–25 illustrate some of the other system outputs: • Figure 23: Congestion Scan—showing impacts (before/after) of a construction project. • Figure 24: Performance Summary Report—showing impacts (before/after) of a signal removal project. • Figure 25: System Trend Map—showing impacts (before/after) of a construction project designed to reduce congestion and travel times to the beach. Cases 109

Introduction • Foundation • Reporting • Insight • Cases Figure 23. Congestion scans depict congestion before and after the completion of a project on the Garden State Parkway. Management and Use of Data for Transportation Performance Management: Guide for Practitioners 110

Introduction • Foundation • Reporting • Insight • Cases Figure 24. Before-and-after performance from the Garden State Parkway signal removal project. Figure 25. NJ Garden State Parkway—before-and-after conditions of a major construction project designed to reduce congestion and travel times to the beach. Cases 111

Introduction • Foundation • Reporting • Insight • Cases Insight: Analyze & Use, Present & Communicate Data Simply providing a set of graphs, maps, and charts from the analytics tools is not sufficient. The agency must write a narrative around the results that interprets the results, points out meaningful observations, and tells the “why” within the performance report. This can be a challenge, and the agency was able to leverage in-house journalists, marketing experts, and other professionals who are trained at communicating performance and otherwise complex transportation analysis for a public audience. For example, both NJDOT and MPOs have developed 11x17 pamphlets that are meant to be handed out to the public and other officials that quickly tell the story of the project (Figures 26 and 27). Figure 26. NJDOT Project Assessment Summary Pamphlet—Part 1. Page 1, insideFront cover Management and Use of Data for Transportation Performance Management: Guide for Practitioners 112

Introduction • Foundation • Reporting • Insight • Cases Figure 27. NJDOT Project Assessment Summary Pamphlet—Part 2. Most of these documents are posted online, and some are also distributed in print form. The new reports and online publications have been well received; the graphics are more engaging than prior reports, and they tell a story that is relatable and understandable to a customer. The agency is now able to update the documents more quickly and have been able to conduct more before-and-after studies in less time than before. This leads to the agency being perceived as more responsive to the public and more capable. The agency is also realizing significant cost savings in using this approach and the data analytics platform. Before having their data fused and available in analytics, they spent upwards of $20k for a before-and-after study with a small consultant team. Now they can conduct the analysis in- house in just a few hours. NJDOT estimates they are saving $475k/year and 4,475 person-hours on conducting these studies annually. Success Factors • Ease of access to the data through graphical user interfaces Cases 113

Introduction • Foundation • Reporting • Insight • Cases • Powerful data visualizations that make data interpretation easy • A champion within the agency that takes the time to learn to use the data/tools • Trust in the data and tools (which comes from having a relatively open system that fully documents how data is interpreted and how calculations are made) Challenges & Lessons • Making the adjustments needed to transition from prior methodologies that relied heavily on modeling and simulation to a new methodology that is based on actual data • Building confidence in the new data and methodologies on the part of both technical staff and agency leadership • Concern that new transparency could potentially show that some projects may not have had the desired impact NJDOT was able to overcome these challenges because of the open lines of communication from those conducting the work up to senior leadership. It was fairly easy to sell management on switching from a modeling and simulation-based methodology (one that relied heavily on assumptions and piecemeal data collection) over to a methodology that leveraged continuous field measurements (probe data) that had been vetted to be extremely accurate. The I-95 Corridor Coalition had an existing program in place to validate data from the probe data providers, which gave NJDOT leadership more confidence in the data. Lastly, agency team members had been participating in a Probe Data Analytics User Group (also hosted by the I-95 Corridor Coalition) that exposed staff to other agencies conducting similar types of analysis using the Probe Data Analytics Suite. Seeing their peers work on similar endeavors gave the agency more confidence in the direction that they were heading. For more information... • Presentation on the NJ DOT Complete Team https://tinyurl.com/completeteam • Use Case Description https://www.ritis.org/usecases/as sessment • RITIS Platform www.ritis.org • Project Prioritization Demo https://vimeo.com/179829037 Management and Use of Data for Transportation Performance Management: Guide for Practitioners 114

Introduction • Foundation • Reporting • Insight • Cases Case I Ohio DOT Winter Performance Management Ohio has spent significant time developing and operationalizing their Transportation System Management and Operations (TSMO) Dashboard, which includes 20 performance measures. Because Ohio experiences a fair share of winter weather, some of their performance measures are dedicated specifically to snow and ice recovery. It can be difficult or impossible to keep roadways clear of snow and ice during storms. However, travelers must be able to continue to utilize the roadways even after major weather events. In light of this challenge, Ohio DOT (ODOT) has developed a specific activity and outcome-based performance measure called “recovery time,” which allows the agency to evaluate their operational strategies to most effectively manage roadways during the winter season, incentivize recovery efforts, and communicate with both management and travelers. Cases 115

Introduction • Foundation • Reporting • Insight • Cases Foundation: Specify & Define, Obtain Data In order to effectively manage operations and winter weather response, ODOT collects speed data along impacted corridors prior to, during, and after snowstorms. In the past, ODOT used traditional static sensors to collect speed information, but with emergence of 3rd-party-provided speed data, ODOT has been able to collect speed data statewide at a much more granular level. The agency also monitors atmospheric and roadway conditions using Roadway Weather Information Stations (RWIS) to identify when the storms begin and end. A snow or ice event begins when 40% of a county’s RWIS stations detect either snow or freezing rain, paired with the following criteria: • The air temperature or pavement temperature is below 34°, AND • The speed drops more than 10 mph below its expected value on at least 25% of designated routes within the county (minimum two). A snow and ice event is considered complete when the following are true: • At least 60% of the county’s RWIS stations are reporting “None” or “Rain” as the precipitation type, AND • The wind speed detected from RWIS stations drops below 15 mph (to account for drifting snow). • ALSO: A new snow and ice event does not begin within two hours. When the above is true, the performance clock starts for each route. The time from the end of the event until speeds recover is called the recovery period. Each county in the state has the goal of two hours for the recovery period on each designated route. The recovery period officially ends once speeds recover to within 10 mph of their expected values for at least an hour. Any route that has not recovered within the two-hour goal will be reported. Management and Use of Data for Transportation Performance Management: Guide for Practitioners 116

Introduction • Foundation • Reporting • Insight • Cases Insight: Analyze & Use Data To view speed information and calculate the recovery period, ODOT has several tools at its disposal. First, they have ability to analyze data using traditional tools and techniques, such as Microsoft Excel and database queries. Second, ODOT also has access to a hosted data visualization platform, Probe Data Analytics, which allows the agency to quickly evaluate the conditions prior to, during, and after the storm. The user can select corridors or regions of interest and animate link-based speed information across multiple time periods to determine when the system is operating at pre-storm speeds. Lastly, the agency has developed its own internal performance measures dashboard that leverages data from the above sources to produce a drill-down-capable summary with supporting graphics. Figure 28 shows the platform’s snow and ice event dashboard. Figure 28. Partial screenshot of ODOT's Snow and Ice Event Dashboard. It can be seen in this figure that in April 2018, District 4 met their performance objective of recovering routes within two hours on only 11 of the 13 routes in their district—thus receiving a recovered score of 85%. As the recovery period became an established measure of performance, the agency was able to implement operational strategies that improved the overall customer satisfaction level. For example, ODOT was able to focus their attention on storm-impacted areas with the slowest recovery period and/or districts that struggled to recover all of their routes to deploy additional roadway treatments for future storms. ODOT also Cases 117

Introduction • Foundation • Reporting • Insight • Cases leveraged existing RWIS data to evaluate roadway conditions (like salinity content, surface temperature, etc.) and a combination of CCTV and speed information (from probe data providers and sensors) to further identify potential sources of slow recovery time and dispatch service patrols to assist stranded motorists or direct travelers to alternate routes through traveler information resources and media campaigns. Their interactive dashboard also allows for drill-down capability— enabling users to look at specific district and specific event performance, as illustrated in Figure 29. This type of drill-down capability made the measures much more insightful as ODOT began to answer the question of why certain districts and routes were performing worse than others. Figure 29. Dashboard drill-down view. Drilling down in the dashboard lets the user see how each event was managed, whether it hit the two-hour recovery period, missed, etc. Clicking on a specific location draws a diagram of the road depicting which segments did not recover soon enough. The dashboard resource view (shown in Figure 30) displays which resources were used: overtime hours, brine, equipment, etc. The district can even include written feedback explaining why it believes it was not able to meet the specified target. Management and Use of Data for Transportation Performance Management: Guide for Practitioners 118

Introduction • Foundation • Reporting • Insight • Cases Figure 30. Dashboard resource view. Success Factors • Customer-focused performance measure with connection to operational actions. ODOT spent a good deal of time developing their TSMO plan and identifying specific measures that would best reflect customer expectations with respect to system performance after a winter storm. Recovery time is the type of measure that is easily communicated to decision makers and general public, yet it also ties well with operational actions that directly influence it. • Use of available data and tools. ODOT was able to utilize existing data sources and tools to calculate this new measure that provided better insight into winter performance management. They also leveraged internal staff to develop their own dashboard and develop computational methods. • Workforce capabilities. ODOT believes it has a large number of younger, computer-savvy engineers who have thoroughly embraced these measures. These staff resources have enabled ODOT to use the measures to make real decisions within the agency. Cases 119

Introduction • Foundation • Reporting • Insight • Cases Challenges & Lessons Defining scope of measure’s effectiveness. As ODOT makes progress in improving recovery periods, they have internally discussed implementation of the same measure during the storm. In effect, the goal could be to bring speeds up to pre-storm values while the storm is ongoing. However, many believe this goal may be in direct conflict with safety-related goals that may require that travelers slow down during poor weather conditions. In this context, recovery period may not be the appropriate measure to track during a storm. Automation. The DOT faced a number of technical challenges associated with the scale of this effort. They had to leverage data and technologies that already existed and find a way to blend these data into a system and processes that could compute measures in real time as a storm moves through an area, provide actionable insights, and allow for operational changes to be made in real time. The level of effort involved in designing and implementing such a system was significant. There has also been a significant amount of manual effort to populate the system and keep it functioning—especially in the early years of development. For more information... • Webinar Recording on Building TSMO Performance Measures https://youtu.be/SCDnwBDhN5 k?t=2345 • FHWA Best Practices for Road Weather Management case study https://ops.fhwa.dot.gov/publica tions/fhwahop12046/rwm16_m ichigan1.htm (this case study is of Michigan DOT, which uses a similar measure as ODOT) • Ohio DOT Point of Contact: Administrator, Office of Traffic Management Management and Use of Data for Transportation Performance Management: Guide for Practitioners 120

Introduction • Foundation • Reporting • Insight • Cases Case J Pennsylvania DOT’s Statewide Transportation Operations Data Warehousing Business Plan The Pennsylvania Department of Transportation (PennDOT) has taken a holistic approach to data planning—involving all possible stakeholders—to try to improve the agency’s data capabilities while reducing costs. This foundational work is key to their long-term success. Cases 121

Introduction • Foundation • Reporting • Insight • Cases Overview Every agency generates transportation-related data and must store those data in a way that enables easy access and management. All too often, agencies collect data in silos. One department generates centerline mapping files in a standalone GIS environment; a second department collects speed and volume data for planning and federal reporting using in-pavement sensors; a third department collects speed data from a mix of probes and above-ground sensors; a fourth department collects and manages toll collection data; and so on. This approach is often organic and usually happens because agencies are large and complex. However, in other agencies, an ad hoc approach can be intentional. Business units fight for resources, become territorial over their own data, or can be uncomfortable with others becoming aware of their efforts. Ad hoc data management (or lack of management) approaches increase agency costs, limit capabilities, and can lead to a toxic culture. The more mature agencies take a holistic view of data collection and management—pooling resources to understand data needs, data assets, data gaps, management, and accessibility. Foundation: Specify & Define Data In 2015, PennDOT undertook a significant effort to lower the cost of doing business through the consolidation of the state’s transportation data assets. Leadership recognized that the transportation community generates a considerable amount of transportation operations data—such as traffic volume data, incident data, asset information, and speed data— without an effective means to share the data between one another. The concept of a Pennsylvania Statewide Transportation Operations Data Warehouse (PA STODW) was born. The idea behind the STODW was to increase planning, operations, and research capabilities within the state, significantly reducing the cost of doing business, dramatically improving agency capabilities, and improving coordination with partners in transit, tolling, freight, local governments, and emergency management agencies. Implementation of a PA STODW would provide access to the best-available transportation operations data in support of the safe, secure, and efficient movement of people and goods on Pennsylvania roads. Figure 31 illustrates an early vision for the data warehouse. Management and Use of Data for Transportation Performance Management: Guide for Practitioners 122

Introduction • Foundation • Reporting • Insight • Cases Figure 31. Early PA STODW system concept. 1. Support objectives-driven, performance-based transportation operations planning. 2. Support a robust transportation operations performance measures program. 3. Provide integrated and interconnected Enterprise IT and ITS technology and data. 4. Support development of data-driven strategies and actions in the planning process that lead to an integrated, efficient transportation system. 5. Improve cooperation and collaboration within PennDOT and between PennDOT and other public agencies for the sharing of transportation operations data. Five specific objectives were established in support of these goals, including: To test the viability of this vision, PennDOT assembled a team to assist in the development of a Concept of Operations (ConOps) and then to develop a business plan with a technical analysis of alternatives and cost analysis. This team, which consisted of leadership within PennDOT’s Bureau of Maintenance and Operations and several dozen stakeholders, developed a set of overarching goals for the PA STODW: Cases 123

Introduction • Foundation • Reporting • Insight • Cases • Establish a PA STODW architecture that is open, receptive and adaptable; is consistent with developing national standards; provides opportunities for private/public partnerships; and encourages and supports interagency cooperation. • Develop and integrate traffic monitoring, traffic surveillance and incident reporting, roadway and roadside equipment, and environmental information throughout Pennsylvania, as appropriate. • Define how operations information is collected, processed, archived, shared, and distributed. • Define the interfaces and information flows among/between subsystems, stakeholder organizations, and PA STODW users. • Assist in developing, prioritizing, and addressing proposed transportation operations technology and data-related investments. In the concept development phase, PennDOT assembled a large group of stakeholders that included nearly every office and division within the agency. PennDOT also involved stakeholders from 18 external agencies, including MPOs, municipalities, transit, commercial freight operators/ organizations, emergency management agencies, etc. During multiple concept development and outreach meetings, these agencies were asked to describe their agency’s data wants and needs and to provide a list of any existing data assets that they might be willing to share. Figure 32 illustrates the deployment concepts that were explored. Management and Use of Data for Transportation Performance Management: Guide for Practitioners 124

Introduction • Foundation • Reporting • Insight • Cases Figure 32. The STODW deployment alternatives concepts. Success Factors • Stakeholder involvement. Through frequent stakeholder meetings and considerable outreach, this project was much more successful than it otherwise would have been. The stakeholder engagement identified data needs and existing capabilities (and inabilities) with respect to data. These stakeholder meetings served to galvanize the state’s data owners and data users and produced a much more effective end result. • Leadership buy-in. The Chief of Traffic Operations in PennDOT had a strong desire to see this project succeed. He was an effective communicator who could easily convey the intent and justification for the project in a way that secured buy-in from others within the agency. Through his leadership, he was able to successfully build a team that shared his vision. • Alignment with existing agency goals. PennDOT’s State Transportation Advisory Committee’s 2015 Transportation Performance report states that “PennDOT is committed to accountability for results and transparency of operations” and that “PennDOT must continue to provide leadership and collaboration to its partners in continuing to modernize transportation products and services.” This foundational data project directly aligned with this commitment and therefore was easier to justify to agency funders. Cases 125

Introduction • Foundation • Reporting • Insight • Cases Challenges & Lessons Conflicting priorities. While nearly everyone in the agency (including many external stakeholders) all viewed this as an important project, it was also seen as a bit of a distraction from other priorities within the department. Lack of immediate results. The result of this first phase was still very foundational. The deliverable was a plan and a list of recommendations. The implemented system that would truly affect people wouldn’t be realized until 1 or 2 years later, and therefore it was a bit difficult to get people to remain excited and committed to the project over a longer time frame after the initial stakeholder engagement activities. Turf wars. When it came to evaluate alternative implementation strategies, certain internal departments would attempt to convince the ConOps developers to sway the recommendations in their favor—giving them ultimate control over the final system. These internal turf wars had the potential to steer the agency toward a potential solution that might not have been optimal. For more information... • Pennsylvania State Transportation Advisory Committee reports & studies web page http://www.talkpatransportatio n.com/advisory- committees/tac-reports-studies • PA STODW Concept of Operations Document and Business Plan and Cost Analysis Report No. FHWA-PA-2016- 006-130108 • PennDOT Point of Contact: Chief of Transportation Operations Management and Use of Data for Transportation Performance Management: Guide for Practitioners 126

Introduction • Foundation • Reporting • Insight • Cases Case K Virginia DOT’s Pavement Monitoring Program The Virginia Department of Transportation’s (VDOT’s) pavement management systems and tools were developed to provide network-level needs assessment and maintenance prioritization support, as well as project-level insights to guide detailed project development and delivery. In 2012, VDOT recognized a need to strengthen the links between the decision- support tools and analysis, and field project selection. The objective was to better ensure the alignment of investment decisions with the established statewide performance targets. This case describes VDOT’s implementation of a pavement performance target monitoring program, which formalized the methodology for establishing district-specific pavement performance targets and the processes for monitoring paving project development and execution toward these district-specific targets. Through routine monitoring of planned paving outcomes in comparison to the established measures, VDOT is able to provide early warning of any anticipated deviations, allowing district pavement managers time to adjust project plans as needed. Cases 127

Introduction • Foundation • Reporting • Insight • Cases Overview VDOT has a well-established pavement management methodology that includes annual pavement condition collection and needs assessment, establishment of statewide pavement condition targets, and a performance-based budgeting process. VDOT’s Central Office Maintenance Division has responsibility for data collection and analysis; districts have primary responsibility for pavement maintenance, rehabilitation, and reconstruction project selection and development. Foundation: Specify & Define, Obtain Data In order to implement the performance target monitoring program, VDOT integrated data between its pavement management system (PMS) and a separate application used at the district level to develop paving contracts—the pavement maintenance scheduling system (PMSS). Planned project information from PMSS is periodically transferred into the PMS, where it can be used to evaluate projected outcomes of planned paving against the established performance targets. Reporting: Store & Manage Data District paving status reports are compiled by Central Office Pavement Management based on PMS analysis of current pavement condition information, district-specific performance and paving targets, as well as the most up-to-date planned project information available from PMSS. The status reports provide a comparison of baseline performance and paving targets with planned and/or actual work accomplishments from the two systems. Reporting is completed at key milestones in the development, execution, and delivery of a paving project: • Routinely during project development, • Immediately following project advertisement and award, and • Routinely during the construction season. The status reports provide aggregated statistics, as discussed below. Baseline Performance Targets These condition targets are established according to district and highway system and are summarized from financially constrained PMS optimization analysis results. These targets are established for the percentage of pavement forecasted to be in “Fair” or better condition (termed the “percent sufficient”) based on the state pavement condition measure. Management and Use of Data for Transportation Performance Management: Guide for Practitioners 128

Introduction • Foundation • Reporting • Insight • Cases Baseline Paving Targets Paving targets are based on the same analysis results used to establish the baseline performance targets, however they identify the total lane miles to be paved within each of VDOT’s four treatment categories: Preventative Maintenance (PM), Corrective Maintenance (CM), Restorative Maintenance (RM), and Reconstruction/Major Rehabilitation (RC). It is important to note that each of these treatment categories represents a range of specific pavement maintenance actions, which are selected on a project-specific basis, at any eligible locations within the network. Planned Paving This is the number of lane miles scheduled in each of the four VDOT treatment categories, calculated based on detailed planned project information imported from the PMSS. These are presented for comparison to the baseline paving targets, as aggregated from district planned pavement maintenance projects. Projected Performance of Planned Paving Based on the district’s planned paving locations and treatment selections (as imported from PMSS), these targets are summarized from PMS condition forecasting based on current conditions, network-level deterioration modeling, and the modeled benefits of planned paving. The results are presented to provide the predicted percentage sufficient for comparison to the baseline performance targets. Flagged Treatment Locations District pavement treatment selections are flagged if they are substantially different from the unconstrained need identified by the PMS based on detailed pavement condition, pavement structure performance, and traffic information available for the location. Specifically, a location is flagged if the district’s planned treatment differs more than a single treatment category from the unconstrained need (e.g., a PM treatment is planned where unconstrained needs analysis suggests RM is needed). This ensures that the district is making reasonable project-level maintenance decisions at the locations selected for investment, while providing the flexibility to allow project-level adjustment where necessary. Actual Work Accomplishments As planned paving is advertised, awarded, and delivered, it is necessary to follow up on actual work accomplishments to ensure these plans are Cases 129

Introduction • Foundation • Reporting • Insight • Cases ultimately constructed. Currently, this process requires pen-and-paper data collection in the field, followed by manual update of the PMS. However, this will become a more viable part of the performance monitoring process as tools to support GIS-based field validation of planned paving and automated data transfer to the PMS are implemented. Insight: Analyze & Use Data These reports are intended to drive project-level decision making in each of the districts toward an optimal “mix of fixes” based on the district’s current pavement condition and pavement maintenance allocations in a way that balances statewide, network-level strategy and decision-support analysis with project-level engineering and decision making. Metrics surrounding planned projects vs. targets are the emphasis in quarterly reporting meetings, as this is where VDOT pavement management staff have the most control over possible outcomes. By providing routine comparison of expected outcomes of planned work to district-specific paving and performance targets, VDOT pavement managers are provided the information necessary to ensure the district’s project-level decision making is in alignment with network-level pavement management strategy and goals. This is accomplished in a manner that is objective and transparent and allows for iterative improvement of both district decision-making processes and Central Office network-level decision-support tools. Figure 33 shows a sample district status report. This report highlights • Current, targeted, and predicted paving performance; • Predicted performance and planned paving as compared to baseline targets; and • Expected outcomes of the district’s current plans. This information is all included as part of the standardized reporting products, which are discussed within regular paving status meetings. Management and Use of Data for Transportation Performance Management: Guide for Practitioners 130

Introduction • Foundation • Reporting • Insight • Cases Figure 33. Sample pavement monitoring report. A district pavement manager is expected to use the reported information to address discrepancies between district-specific project plans and network-level goals for their program. In this example, the district manager would quickly identify that current plans achieve the Cases 131

Introduction • Foundation • Reporting • Insight • Cases performance goals for the Interstate system, however, the primary system will underperform if current plans are not improved. By reviewing the differences between planned and targeted paving by treatment category, the pavement manager should see that a reasonable explanation for the underperformance of the primary system is that the current maintenance program is overemphasizing RC at the expense of low cost, effective PM and CM treatments. By acting to reduce planned RC to allow funding of more cost-effective maintenance strategies, the district should be able to bring the predicted performance of the primary system closer to the desired performance target. Additionally, a tabular report is provided that flags any locations where the district-selected maintenance treatment does not reasonably align with the PMS unconstrained treatment recommendations. In this case, the district pavement manager could review this report to identify locations where a costlier treatment has been planned over the PMS- recommended maintenance treatment. This may allow for efficiencies to be identified, generating savings that could be used for additional maintenance on the primary system. Success Factors • Planning and data modeling to facilitate system integration. Through iterative improvement of both PMS and PMSS data models over the course of several years, automated processes to transfer information between these two systems have been developed. • Tapping into available data sources. Optimal selection, planning, and execution of maintenance projects are the most direct ways for a DOT to improve asset conditions on their network. VDOT identified the information available during project development and execution that could be used to predict the influence of planned paving on the network-level performance and made this information available to PMS analytical tools. This allowed the department to integrate network analysis with project decision making without additional data collection or reporting burden to district staff. • Linking paving schedules to performance targets. Arming district pavement managers with the information needed to understand network-level implications of project-level investment decisions reinforces good pavement management practice. Field input generated through elevated attention to the PMS analysis also exposed previously unrecognized opportunities to improve the decision-support tools. Management and Use of Data for Transportation Performance Management: Guide for Practitioners 132

Introduction • Foundation • Reporting • Insight • Cases • Building report review into business processes. Pavement target monitoring analysis is integrated into the project development process through an easily consumed reporting format and in a way that is respectful of field decision makers’ local experience and expertise. The information in these reports is the focus of quarterly performance meetings, as well as routine status meetings with Central Office Pavement Management program leaders, which ensures district attention and action. Challenges & Lessons VDOT faced challenges bridging network-level analysis to project-level decisions in a manner that would support routine execution as part of the pavement target monitoring process. Bridging PMS analysis to project decision making. VDOT worked to balance the use of the network-level analysis to ensure that decision quality would not be reduced through overly prescriptive use of network-level analysis and that distrust of modeling output would not develop where network analysis did not align with the project-level observations. Automation of data exchange. An efficient process to translate planned paving information from PMSS to the PMS was required. Developing a solution required significant effort on the part of IT and business staff to review and update existing data models, as well as address location referencing issues between the two systems. Proactive engagement of IT and/or Enterprise Architecture staff to help identify formal, sustainable solutions to the exchange of information between established IT systems is recommended. For more information... • VDOT Point of Contact: Maintenance Division Assistant Division Administrator, State Infrastructure Cases 133

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Recent federal legislation has established requirements for agencies to set performance targets and report on safety, pavement, and bridge conditions; transit asset state of good repair; system performance; freight; and mobile source emissions. These requirements have resulted in increased visibility and attention to Transportation Performance Management (TPM) and increased awareness of the importance of data within that process.

The TRB National Cooperative Highway Research Program's NCHRP Report 920: Management and Use of Data for Transportation Performance Management: Guide for Practitioners provides practical guidance to transportation agencies to help improve their use of data for performance management.

The guidance is organized around six data life-cycle stages and includes a discussion of what is involved in implementing each step and some of the critical choices to be made; a synthesis of key points in the form of “Dos and Don’ts” checklists that can be used to assess agency capabilities and identify opportunities for improvement; and illustrative examples.

While this guide draws on many examples related to the federally defined TPM areas (safety, pavement, bridge, and system performance), it does not provide official guidance for MAP-21/FAST Act target setting or reporting. It provides a framework for assessing current data management practices and a source of ideas for practice improvement. Its purpose is to promote practices that will enable agencies to go beyond meeting reporting requirements, to get valuable insights from data that can be used to boost agency results.

An additional resource to the guide is a downloadable report: Developing National Performance Management Data Strategies to Address Data Gaps, Standards, and Quality: Final Research Report.

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