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1 New Jersey Pilot Study Testing Potential MAP-21 System Performance Measures for Two Corridors Executive Summary October 2014

2 ABOUT THE NJTPA THE NJTPA IS THE FEDERALLY AUTHORIZED Metropolitan Planning Organization for 6.6 million people in the 13-county northern New Jersey region. Each year, the NJTPA oversees more than $2 billion in transportation improvement projects and provides a forum for interagency cooperation and public input. It also sponsors and conducts studies, assists county planning agencies and monitors compliance with national air quality goals. DISCLAIMER THIS DOCUMENT WAS PREPARED by the North Jersey Transportation Planning Authority, Inc. with funding from the Federal Transit Administration and the Federal Highway Administration and technical contributions from the New Jersey Department of Transportation. The NJTPA is solely responsible for its content. The document is meant for informational purposes only. PROJECT TEAM BRIAN FINEMAN, NJTPA, Director, Systems Planning KEITH MILLER, NJTPA, Principal Planner, Data Analysis and Forecasting SUTAPA BHATTARCHARJEE, NJTPA, Principal Transportation Planner JOHN ALLEN, NJDOT, Section Chief, Commuter/Mobility Strategies

3 New Jersey Pilot Study Testing Potential MAP-21 System Performance Measures for Two Corridors Executive Summary Since the passage of the Moving Ahead for Progress in the 21st Century Act (MAP-21, P.L ), the transportation and planning community has engaged in an extensive national discussion regarding the legislation s performance management provisions. State departments of transportation and Metropolitan Planning Organizations (MPOs) around the country will be required to conduct performance-based planning and programming in order to make use of federal transportation funds. Specifically, states and MPOs will need to apply national measures of transportation system performance and infrastructure condition through data monitoring, target setting, and integration within plans and programs. Measures related to system performance, reliability, congestion, and freight movement involve particularly challenging analytical and policy issues. Considering the importance of these issues in the context of regional mobility, sustainable economic growth, and environmental preservation, the North Jersey Transportation Planning Authority (NJTPA) and the New Jersey Department of Transportation (NJDOT) set out to investigate system performance measures that may emerge as the U.S. Department of Transportation (USDOT) promulgates rules implementing MAP-21. This report offers our study findings. Formulated as a pilot test, our examination included both practical matters in calculating performance measures, and what those measures might ultimately reveal about actual system performance. The national performance measures will apply to the overall Interstate System, the non-interstate National Highway System (NHS), and freight movement on the Interstate System. We therefore focused on two illustrative New Jersey corridors: I-78 and NJ Route 18, an interstate highway and a principal arterial roadway on the NHS. We based our measures of system delay and reliability and the methods used to calculate them on those proposed by national transportation stakeholders, including the American Association of State Highway and Transportation Officials (AASHTO, through its Standing Committee on Performance Measurement, or SCOPM). PERFORMANCE MEASURES Delay on roadways is a common measure of performance and traffic congestion. Time spent traveling on a road in excess of an established threshold is counted as delay. Reliability of the system considers how travel time varies from day to day. An unreliable system obliges travelers to either budget extra time because their trips are unpredictable, or suffer the consequences of late arrivals at work, school, appointments, etc. i

4 Our findings illuminate differences that emerge from various alternative performance measure formulations, both quantitatively (with different numerical results), and qualitatively (how they reflect the underlying traveling conditions on roadways and the planning context of the places that surround them). We also report on data gaps and challenges, recognizing the approaching need to report on entire regional and state systems. Finally, we make some specific recommendations regarding the methodologies and suggest additional work that we think is critical in fully understanding the implications of MAP-21 s system performance program. To that end, here are some key observations from the study: User Perspective Fundamental to system performance measures is the choice of the user perspective. For example, we tabulated hours of delay (over the course of a year) for both vehicle- and passengerbased trips. We believe that the latter is more indicative of our mission (moving people and goods), and would be sensitive to improvements that serve more traveling customers. A personbased approach does require (appropriate) attention to data for public transit and shared ride modes. Similarly, we brought volumes of truck traffic into the equation, illustrating where delays affect freight movement. This measure formulation would be sensitive to system changes that reduce delays in locations and times when trucks are traveling. Thresholds and Delay Performance measure calculations for both delay and reliability are highly dependent on choices of thresholds. For example, we found that a good case can be made that a free-flow threshold which considers any extra time spent traveling due to interference from other vehicles to be counted as delay is an untenable standard for planning purposes, encouraging inefficient use of expensive infrastructure. Counting delay in this way yields much larger delay values than use of other thresholds for acceptable travel times. Thresholds that vary from place to place can help planners and decision-makers take context into account, recognizing that some congestion in more densely developed areas is an unavoidable aspect of metropolitan life and economic activity. Our comparisons of different thresholds for delay suggest that tailoring to specific types of places (urban, suburban, rural, etc.) can yield values of delay that are more representative of local planning objectives. For other choices of thresholds we took into account different expectations for peak and offpeak travel as well as allowing for the greatest throughput of vehicles, with results varying accordingly (i.e., more or less delay counted). We found that thresholds tied to maximizing throughput can yield reasonable results, and may also motivate efficient use of existing infrastructure. Reliability The reliability indexes we tested are ratios of an extreme (or unexpected) travel time and a threshold (or expected) travel time. In other words, how many times longer does travel take in unexpected conditions compared to what the traveler expects? ii

5 As ratios, reliability measures are more challenging conceptually and mathematically. Rather than simply adding up total hours of delay over the course of a year, a reliability index quantifies how travel times vary from day to day. By definition this implies that, to evaluate an entire system, an averaging of some kind is required from place to place throughout the system and throughout the year. We tested different approaches to weighting when we averaged reliability values for corridor segments. Weighting by person or vehicle hours and miles traveled emphasize different dimensions and thus different policy intent. Weighting by truck and bus volumes can help to emphasize the reliability of those modes. Our own reliability index formulation takes into account expected or routine travel times by time of day. This method yields more moderate reliability values and may be more appropriate than using the same expected travel time at all times of the day. With particular measure formulations, we were able to observe roadway segments in our test that were reliable but routinely experience significant delay. The ability to make this distinction seems important from a policy standpoint, especially as reliability is such a critical issue to the traveling public. We note (but did not test) alternate considerations for such extreme times. We used travel times that are exceeded in only one out of five days (80 th percentiles, as suggested by SCOPM). One might choose even more extreme, longer travel times that occur even less frequently, such as one out of twenty days (95 th percentiles). This choice should reflect what types of problems are being targeted regular unreliability or rarer but more significant breakdowns. It may be appropriate to use different values of thresholds for reliability and delay. This is due to the differing nature of what thresholds represent in reliability measures (expected travel times that are compared with extreme travel times) versus what they represent in delay measures (travel times above which travelers experience delay). Data and Analysis We were able to successfully apply archived operations, traffic volume, bus, truck, and vehicle occupancy data to calculate the measures of interest using resources that we have available in New Jersey. We made extensive use of the I-95 Corridor Coalition s data (from the Vehicle Probe Project), along with Geographic Information Systems (GIS) and database tools. The substantial level of effort required for our two corridors strongly suggests that more powerful analytical and data management tools will be needed for regional and statewide analysis. To join the data from disparate sources, we needed to apply geographic conflation techniques. Future data and GIS development should better integrate these sources to support performance measure calculation. We identified several important data limitations, not least of which being the coarser data available for traffic volumes. While newly available operations data provides continuous speed and travel time information, volume data is typically extrapolated from discrete traffic counting. Also, additional data will be needed (at least in New Jersey) to cover MAP-21 s expanded NHS designations. iii

6 We note the critical attention needed to examine how performance measures change over time, which was beyond the scope of this study. This is the primary function of performance measures in planning and programming. It will involve a whole set of additional analytical issues, such as maintaining data consistency, ensuring sensitivity to changes of interest (distinguishing externalities from the effects of investments), and addressing the timeliness of data availability. Performance measures must be able to inform planners and decision-makers about actual impacts of implemented projects and programs. As we await the USDOT establishment of the national system performance measures, our pilot test has readied us for some of the inevitable challenges that will emerge. It has also helped us to clarify issues regarding the reporting of performance measures and the communication of their meaning. We fully recognize, however, that we have merely scratched the surface in investigating the full ramifications of particular performance measures. We trust that MPOs and state transportation departments like ourselves will continue to be engaged in developing and fine-tuning performance measures that are effective and meaningful both nationally and for our local jurisdictions. iv

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