New Jersey Pilot Study

Transcription

1 New Jersey Pilot Study Testing Potential MAP-21 System Performance Measures for Two Corridors Summary Report 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. NEW JERSEY PILOT STUDY 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 NEW JERSEY PILOT STUDY

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. NEW JERSEY PILOT STUDY 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 NEW JERSEY PILOT STUDY

7 Contents Executive Summary... i User Perspective... ii Thresholds and Delay... ii Reliability... ii Data and Analysis... iii Background... 1 MAP-21 Requirements... 2 Proposed Performance Measures in the Field... 2 Context in New Jersey... 3 Supportive Planning Context... 4 Study Approach... 6 General Approach... 6 Pilot Corridors: Selection and Segmentation... 7 Corridor Descriptions... 8 Interstate New Jersey Route Data: Sources, Gathering & Processing Travel Time Thresholds: Defining and Evaluating Free-flow Travel Time Median Travel Time over All Hours and Days Median Travel Time by Day of Week and Hour of Day Maximum Throughput Travel Time Acceptable Travel Time Threshold Consistency Across Agencies and Between Measures Delay Performance Measures Annual Delay based on Average Week (SCOPM Formulation) Person Hours of Delay vs. Vehicle Hours of Delay Truck and Bus Delay Incorporating Bus and Truck Volumes Delay by Vehicle Type Effects of Thresholds on Delay Values Free-flow Threshold Yearly Median Threshold Day/Hour Median Threshold Maximum Throughput Threshold Acceptable Speed Threshold NEW JERSEY PILOT STUDY v

8 Actual Annual Total Delay (Alternate Formulation) Calculation Method Effect of Formulation on Delay Values Reliability Performance Measures Reliability Index (SCOPM Formulation) Calculation method Example Thresholds Effect of Threshold Variation on Reliability Index Dynamic Reliability Index (Alternate Formulation) Calculation method Example Results Aggregation of Reliability Values Method 1: Weighted Average of Reliability by Segment Method 2: Direct Corridor/Sub-corridor Reliability Calculation Truck & Bus Reliability Observations: Challenges and Lessons Learned User Perspective Analysis Tools Data Availability Thresholds and Delay Reliability Aggregation Arterials Potential Follow-up Work Appendices A. Draft Scope of Work B. Detailed Tables of Results by Sub-corridor and Corridor C. Downloading and Processing Archived Travel Time Data D. Conflation E. Weigh-in-Motion Data Processing F. NJ TRANSIT General Transit Feed Specification Processing G. Plan4Safety Data Processing vi NEW JERSEY PILOT STUDY

9 Background Since the passage of the Moving Ahead for Progress in the 21st Century Act (MAP-21, P.L ) on July 6, 2012, the transportation and planning community has engaged in an extensive national discussion. This discussion has centered around the legislation s performance management provisions and the specific standardized national performance measures that will be promulgated by the US Department of Transportation (USDOT). 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 these national measures of transportation system performance and infrastructure condition through data monitoring, target setting, and integration within their plans and programs. Among the required MAP-21 performance measures, those related to system performance, reliability, congestion, and freight movement have drawn much attention because they involve particularly challenging analytical and policy issues. Considering the importance of these issues in the context of regional mobility, sustainable economic growth, and environmental protection, 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 in the USDOT rulemaking. 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). 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 SCOPM methodologies and suggest additional work that we think is critical in fully understanding the implications of MAP-21 s system performance program. NEW JERSEY PILOT STUDY 1

10 MAP-21 Requirements MAP-21 established seven national transportation goals upon which to focus the overall Federal-aid highway program. As part of the MAP-21 transportation performance management process, USDOT will be establishing national performance measures, associated with specific FHWA and FTA funding programs. While there is not a one-to-one correspondence between the goals and measures in Transportation Performance Management Process (Source: USDOT) MAP-21, there are clear connections. The table below lists all seven MAP-21 goals and shows those MAP-21 programs for which performance measure areas are identified related to system performance the focus of this report including reliability, congestion, and freight movement. All MAP-21 National goals Safety Infrastructure Condition Congestion Reduction System Reliability Freight Movement & Economic Vitality Environmental Sustainability Reduced Project Delivery Delays System Performance-Related MAP-21 Programs and Performance Measure Areas (Asset Management and Safety areas are not included here) National Highway Performance Program Interstate System performance Non-Interstate National Highway System performance Congestion Mitigation & Air Quality (CMAQ) Program Traffic Congestion Freight Movement on the Interstate System Proposed Performance Measures in the Field Specific national performance measures for these areas are forthcoming from USDOT at the time this report is being prepared. However, USDOT has encouraged input during the rulemaking process and numerous stakeholder groups have weighed in 1. Of course, a good deal of consideration is going into interpreting just what the concepts of system performance or reliability actually mean. The establishment of specific measures to represent the performance areas will, by definition, pin those meanings down. Notable among the commenters to USDOT is a set of performance measure recommendations compiled by the American Association of State Highway and Transportation Officials (AASHTO, through its Standing Committee on Performance Measurement, or SCOPM) in its Findings on National Performance 1 See, for example, FHWA s record of MAP-21 webinars, 2 NEW JERSEY PILOT STUDY

11 Measures 2. The SCOPM recommendations are clearly well thought out and have rightfully gained significant attention in the field. For Interstate and National Highway System (NHS) system performance and for freight performance, they suggest specific formulations of travel delay and reliability measures. The SCOPM measures were the key starting point for our pilot study. We have also highlighted other important aspects of system performance that transportation planners have been emphasizing. These include a focus on total travel times per trip, as raised by Transportation For America 3 (T4A) and total regional travel times, suggested in research by the Texas A&M Transportation Institute 4. How efficiently the highway system is being used seems an essential consideration for fiscally constrained transportation planning and the concept of throughput productivity used extensively by Washington State Department of Transportation (WSDOT) 5 and noted by T4A and others was also brought into our pilot study. Context in New Jersey Our effort emerged from a discussion about performance measure challenges in the kick-off meeting for the New Jersey MAP-21 Performance Measure Committee, a group hosted by NJDOT and involving all three New Jersey MPOs, NJ TRANSIT (the state s transit provider), FHWA, and FTA. In general, New Jersey agencies appear well poised to address the new MAP-21 requirements, with a long history of collaboration and cooperation in developing and applying data, performance measures, and analysis in planning and project development. However, at the meeting several attendees expressed some concern about how USDOT will shape the national measures of system performance, and whether those measures will adequately and fairly reflect roadway conditions and potential improvements made within the state and its metropolitan regions. New Jersey is the most densely populated state in the nation, both blessed and challenged with a very mature (and aging) transportation system. Situated between two major U.S. cities, New York City and Philadelphia, and with major economic centers of its own (including major cities, the shore, ports, airports, warehousing, and pharmaceutical companies), the state experiences enormous traffic flows and movement of persons and freight on an extensive, interconnected multimodal network. At the same time, the state grapples (as do others) with the imperative of sustainable (and smart) economic growth and environmental preservation. The role that transportation plays in both is clear, but the choices of performance measures (and targets) will dramatically affect the understanding of how transportation is functioning in this context. Questions include: 2 Task Force Findings on National Level Measures FINAL ( ).pdf 3 Making the Most of MAP-21, Transportation For America, Developing a Total Peak Period Travel Time Performance Measure: An Updated Concept Paper, T.J. Lomax, D.L. Schrank, P.H. Lasley, W.L. Eisele., WSDOT s Congestion Measurement Approach: Evaluating System Performance, NEW JERSEY PILOT STUDY 3

12 Will these performance measures highlight the right issues in an area that is already heavily congested and likely to remain that way? Will successful increases of efficiency in the system through shifts to public transit and sharedride modes be adequately reflected by the national performance measures? Will summary region-wide (or statewide) measures be sensitive to the impacts of targeted (and, of necessity, affordable) improvements that address small parts of a very large transportation network? Will uniform system performance measures be able to recognize and appropriately reflect the diverse urban, suburban, and rural landscapes in New Jersey? We were also concerned about New Jersey state and MPO technical capabilities. The NJTPA, NJDOT, NJ TRANSIT, and other New Jersey MPOs and partner agencies have access to and have invested in significant data resources and analysis tools. We have worked to stay at the forefront of new and powerful datasets, including through partnerships with the I-95 Corridor Coalition. Even given this, however, it was not at all clear what level of effort might be involved in processing whatever measures may be established and required. Clearly, using archived operations data (integrated with other necessary data, like traffic volumes, auto occupancy, and transit ridership) to evaluate the NHS within New Jersey is an immense undertaking. The sheer amount of data (hundreds of millions of records annually), the data storage and management requirements, and the development of essential tools and techniques will be unprecedented. Significant barriers and decisions exist in developing the required performance-based planning protocols, analytics, and related technology. These, and other questions and concerns, motivated this study. At its conclusion, many still remain. However, we believe the work described here has begun to illuminate this type of range of critical issues that are upon us in the transportation planning field. Supportive Planning Context As agencies that will be responsible for applying the national performance measures, MPOs and state Departments of Transportation are engaged in cross-cutting planning and infrastructure management activities. The interplay among the transportation goals established by MAP-21 and other public goals is critical. In fact, the need to coordinate activities among different focus areas in the public and private sectors is widely recognized including transportation, housing, economic development, sustainability, education, and so on. Planning efforts in and around New Jersey greatly support this kind of coordination. The NJTPA is a principal collaborator in the multi-agency Together North Jersey (TNJ) initiative, funded in part by a Housing and Urban Development (HUD) Sustainable Communities planning grant. TNJ involves dozens of partner 4 NEW JERSEY PILOT STUDY

13 agencies and stakeholders (including NJDOT) and is bringing together policy makers, professionals, experts, and the public in a collaborative effort across ten priority topic areas facing northern New Jersey 6. NJDOT coordinates across state agencies, including with the New Jersey Office of Planning Advocacy charged with developing the New Jersey s State Strategic Plan/State Development and Redevelopment Plan. New Jersey agencies already make extensive of performance based planning and asset management techniques. NJDOT oversees a range of data-driven management systems, covering areas including safety, freight, congestion, pavement, and bridges. NJ TRANSIT collects a wide range of transit-specific data, conducts a variety of performance-based studies, and maintains a Scorecard indicator system to report performance to the public. NJTPA and other MPOs operate congestion management processes, conduct air quality conformity analyses, study freight and safety issues, and oversee a performancedriven project development and prioritization process. MPO scenario planning utilizes performance measures, including scenario planning work underway for the TNJ effort. New local asset management initiatives and a revisiting and expansion of the New Jersey Intelligent Transportation Systems Architecture are all focused on key performance measures. Additionally, the current environment of fiscal constraint and a Fix It First philosophy at NJDOT has shifted the strategic focus for congestion relief and improved mobility: away from major capacity increases and new alignments, and towards targeting system inefficiencies and demand (with strategies including bottleneck mitigation, improved systems operations, and travel demand management). Particular emphasis is currently on addressing high-need signalized intersections, optimizing signalized corridors and intelligent transportation systems (ITS)/transportation systems management (TSM) Subsequently, planners and operators are cooperating in a number of areas to better facilitate linkages between regional transportation planning & investment decision-making, and transportation systems management & operations (TSMO). This collaborative construct has been dubbed complete team and will aid in the execution of MAP-21 requirements. We have recognized from the outset that each of these (and other) efforts is essential to relate to the burgeoning performance based planning arena. While we will not be able to explicitly make all such connections for the study described here, we have tried to maintain an awareness of the broader planning context and the requirement for information to flow to and from these related tasks. 6 TNJ was formed in 2012 by Edward J. Bloustein School for Planning and Public Policy at Rutgers University (Rutgers-Bloustein), the North Jersey Transportation Planning Authority (NJTPA), jurisdictions from throughout the NJTPA region, NJ TRANSIT, the NJ Office of Planning Advocacy (NJOPA), Housing and Community Development Network of New Jersey (HCDN-NJ), the Sustainability Institute/Sustainable Jersey at The College of New Jersey; NJ Future; Building One New Jersey; PlanSmart NJ; and Regional Plan Association (RPA). See NEW JERSEY PILOT STUDY 5

14 Study Approach In this pilot study, we have focused on two of the national goal areas transportation system performance and freight system performance. For both of those goal areas, SCOPM recommended two distinct measures: Annual Hours of Delay (AHD) and a Reliability Index (RI 80). AHD is a measure of total delay, and represents the increase in travel time due to traffic congestion 7. RI 80 is a measure of nonrecurring delay, and represents the variability in travel times experienced by the traveling public. We have calculated these measures for not only for passenger vehicles, but also for trucks 8 and buses, to help gauge the usefulness of these performance measures as they relate to freight and transit issues. Using the proposed methodologies in the SCOPM Task Force Findings on National-Level Performance Measures as a guide, we developed a detailed proposed scope outline (a collaborative effort between NJDOT and the NJTPA), which can be found in Appendix A. While we did not end up performing all of the elements in the proposed scope, we did manage to complete all of the major tasks. General Approach The initial purpose of this pilot project was to test the overall validity of the proposed methodologies for calculating, summarizing, and interpreting performance measure results; evaluate whether we could calculate potential performance measures using available data; and consider whether these performance measures could reflect a multi-modal, customer-oriented, and context-sensitive planning focus. An important starting point was the set of suggested measures produced by SCOPM, although we incorporated principles and suggestions from other perspectives as well. Importantly, we included measures of people rather than just vehicles, variations based on urban/suburban/rural place differences, and public transit and freight elements. We wanted to examine concepts of acceptable levels of delay, along with approaches that focused on maximizing the efficiency of the roadway system. With this approach, the study covered the following aspects specific to performance measurement: testing the SCOPM-proposed system performance measures, developing and evaluating alternative formulations, assessing various thresholds associated with these measures, and exploring methods to aggregate measures spatially. We tested the SCOPM calculation methods for AHD and RI 80, along with several variations to explore different thresholds and units that address person-based measures in addition to vehicle-based ones. We developed and tested an alternate formulation for each measure category as well. Of note is an alternative formulation for reliability (considering maximum travel time unreliability TTR Max), which recognizes how median and more extreme travel times vary throughout the day. 7 By definition, AHD includes both recurring (expected) and nonrecurring (unexpected) delay. 8 SCOPM recommended that both Annual Hours of Truck Delay (AHTD) and a Truck Reliability Index (RI 80) be used as freight system performance measures. 6 NEW JERSEY PILOT STUDY

15 We also wanted to focus on these areas: ensure measures can be calculated with available data; get a sense of the range of values, and what the values are telling us; research data automation methods; document assumptions, challenges, barriers, and workarounds; and develop this report, with particular emphasis on the procedures, results, lessons learned and recommendations. Reflecting the above focus areas, we wanted to ensure that the performance measures were: practical; calculable given available data, technical resources, effort, etc.; meaningful and appropriate, given the context of national, state and regional policy goals; adaptable to reflect local context; suitable for target setting; and able to track change over time, reflecting the impact of investment impacts. We also recognize that performance measures required for MAP-21 may be distinct from other performance-based planning and programming measures that an agency may use. In other words, agencies may need to supplement the MAP-21 performance measures with additional measures in order to reflect local policies and priorities. Note that, because we examined many possibilities for numerous variables (up to 5 different thresholds, 5 different measurement units, several different aggregation methods, and 2 calculation methods for each performance measure), presentation of the results can get unwieldy. Thus, the main body of this report does not contain all results for all of the combinations of variable choices. We have attempted to present tables and graphs that only vary one parameter at a time, holding all other parameters constant, in order to illustrate the impact of each variable on the results. We have included detailed tables of many of the combinations (at the sub-corridor and corridor level) in Appendix B. Pilot Corridors: Selection and Segmentation Considering the limited resources and time, we chose two corridors within the NJTPA region: Interstate 78 (I-78) and NJ Route 18 (a state highway). These corridors an interstate and a freeway/arterial represent a diversity of conditions: from rural to urban, sections that include local and express lanes, both tolled and free roadways, freeways and arterials, and signalized and limited access. We selected these two corridors based on their regional importance (so that most people familiar with the region would recognize them), their unique features (so that we could test a variety of conditions), and our familiarity with them (to help us interpret the results). NEW JERSEY PILOT STUDY 7

16 We then subdivided each corridor into four sub-corridors (lettered A through D). We conducted this subdivision process with simplicity and common sense in mind, considering adjoining land use, travel intensity and major intersection or interchange points with other highways. The sub-corridors were useful for comparing results from the various alternatives. The lowest level of spatial detail for which archived travel time data is available is the traffic message channel (TMC) link 9. We calculated all performance measures on a TMC link level, and aggregated them up to sub-corridors and corridors. These aggregations allowed for comparing results for different alternatives (e.g.; various user-defined thresholds) and also highlighted how aggregation changes telling the story spatially (from TMC to sub-corridor to corridor). For the delay measures, this spatial aggregation was rather straightforward, in that we could simply sum up the number of vehicle- or passenger-hours along a sub-corridor or corridor. However, the process for the reliability measures was not as straightforward, because they are unitless ratios of time, and cannot simply be added together. We tested two different methods of aggregation: calculating a weighted average based on a variety of weights (including travel time, link length, and daily vehicle- or person-hours 10 of travel, among others), and direct calculation of the measures at the sub-corridor and corridor level (see further discussion below). Corridor Descriptions Following are descriptions of each corridor and sub-corridor, to give some details of conditions on the ground, and which may be useful in interpreting the results. Interstate 78 I-78 (see Figure 1Figure 1) is an east west route stretching 67.8 miles (109 km) in the northern part of the state from the Pennsylvania border (the I-78 Toll Bridge over the Delaware River) to New York City (the Holland Tunnel under the Hudson River). Starting in the west, I- 78 begins in the rural areas of western New Jersey (Warren and Hunterdon counties), and then enters the suburban areas in Somerset County. The road crosses the Watchung Mountains, widening into a local-express lane configuration at Route 24 as it continues through urban areas to Newark. Here, I-78 intersects the mainline of the New Jersey Turnpike (I-95) and becomes the Newark Bay Extension, crossing the Newark Bay Bridge and continuing to Jersey City. Within Jersey City, I-78 joins Route 139 and follows a one-way pair of surface streets to the Holland Tunnel. 9 See, for example, 10 Vehicle-hours of travel refers to the number of hours that vehicles travel on the network (calculated by multiplying the number of vehicles on a facility times its length), whereas person-hours of travel refers to the number of hours that individuals travel on the network (calculated by multiplying the number of people in vehicles on a facility times its length). 8 NEW JERSEY PILOT STUDY

17 Phillipsburg Warren 78-A Hunterdon Miles Figure 1: I-78 corridor and sub-corridors K Morris %&h( Somerset 78-B Union Elizabeth 78-C!"c$ 78-D I Sub-corridor 78A (PA Boarder to I-287, 30.8 miles, 65 MPH speed limit, 50, ,000 Average Annual Daily Traffic (AADT)) I-78 enters New Jersey from Pennsylvania on the Interstate 78 Toll Bridge over the Delaware River, as a 65 MPH, six-lane freeway into agricultural areas. The freeway joins with US 22(at mile 3.8) and continues east eventually entering more commercial areas. At mile 18.9, US 22 splits from I-78, and I-78 then passes over New Jersey Transit s Raritan Valley Line and runs through rural areas with increasing suburban development. After crossing the Lamington River at mile 28.7, I-78 crosses into Somerset County, continuing east through more woods and farms with some suburban residential areas. Sub-corridor 78A ends at the interchange with I-287 (mile 30.8), which serves as a bypass around New York City. I-78 in Hunterdon County (Source: Google Street View) Sub-corridor 78B (I-287 to the Garden State Parkway, 22.6 miles, 65 MPH speed limit, 70, ,000 AADT) At the beginning of this sub-corridor, I-78 carries four eastbound lanes and three westbound lanes as the median widens. The road enters wooded suburban areas and crosses Second Watchung Mountain. The eastbound direction narrows back to three lanes before the interchange with CR 525, at mile I-78 through the Watchung Reservation, with a bridge designed for animals to cross the road (Source: Wikipedia) The freeway crosses into Union County, and then runs between Second Watchung Mountain to the northwest and the Watchung Reservation to the southeast. Here, I- 78 heads away from the Watchung Reservation and into NEW JERSEY PILOT STUDY 9

18 more suburban surroundings, eventually reaching the Route 24 interchange, where suburban development becomes denser. At Route 24 (mile 49), I-78 divides into local and express lanes, initially with three express and three local lanes eastbound and two express and three local lanes westbound. In this section of the highway, most access is via the local lanes. Past mile 50.6, there are two express lanes in both directions. Sub-corridor 78B ends at interchange with the Garden State Parkway (mile 53.3). Sub-corridor 78C (Garden State Parkway to the NJ Turnpike, 5.4 miles, 65 MPH speed limit, 120, ,000 AADT) I-78 heads into more urbanized settings, continuing into Newark (still with express and local lanes). Upon entering Newark, the freeway passes near urban neighborhoods, to the final interchange on the free part of I-78. Called the Newark Airport Interchange, this massive complex to the north of the Newark Liberty International Airport has ramps to and from US 1/9, US 22, Route 21, and many local roads. Several ramps provide access to and from the express lanes. At the east end of sub-corridor 78C (mile 58.6), the local and express lanes rejoin at the toll barrier for the New Jersey Turnpike. Sub-corridor 78D (NJ Turnpike to the Holland Tunnel, 9.0 miles, 55 MPH speed limit, then varies from 25 to 35 MPH near the entrance to the Holland Tunnel, 60, ,000 AADT) I-78 eastbound heading onto the Newark Bay Bridge (Source: Wikipedia) I-78 in Newark (Source: Google Street View) An interchange just beyond the toll barrier provides full access to I-95, the mainline of the New Jersey Turnpike. I- 78 here becomes a four-lane highway, passing by the Port Newark-Elizabeth Marine Terminal and crossing the Newark Bay on the Newark Bay Bridge. The freeway turns northeast on an elevated alignment and passes industrial areas of Jersey City. The turnpike ends at exit 14C (the number given to the toll plaza at the end of the turnpike extension). After the toll plaza, I-78 heads down to surface level and merges with Route 139. From here, I-78 and Route 139 pass through business areas as a one-way pair that follows six-lane 12th Street eastbound and six-lane 14th Street westbound, both with traffic lights (an example of a non limited access section of Interstate Highway). After the toll plaza for the Holland Tunnel (mile 67.1), I-78 enters the Holland Tunnel under the Hudson River, which carries two lanes in each direction. Route 139 ends at the New Jersey/New York state line within the tunnel and I-78 continues into Manhattan. 10 NEW JERSEY PILOT STUDY

19 New Jersey Route 18 NJ 18 (see Figure 2) is a north-south, 40.2-mile (65-km) long state highway stretching from an interchange with Route 138 in Monmouth County to Hoes Lane in Middlesex County. Much of the route is a limited-access freeway, including the entire portion in Monmouth County and the northern end through New Brunswick and Piscataway. The remainder of the route is a multi-lane divided highway, with a number of signalized intersections within the section from Old Bridge to New Brunswick. The route is unique in that it serves a variety of trip types and destinations: from the shore area (Belmar to Asbury Park) to the city of New Brunswick, where corporate headquarters of global pharmaceutical companies (Johnson & Johnson, Bristol- Myers Squibb) and Rutgers University are located. Somerset Middlesex!"c$ 18-D Sayreville 18-C?Í 18-B Monmouth 18-A Miles I Figure 2: NJ 18 corridor and sub-corridors Freehold Morganville 56 Middletown Tinton Falls Sub-corridor 18A (NJ 138 to the Garden State Parkway, 9.2 miles, 65 MPH speed limit reducing to 55 MPH in the vicinity of the GSP interchange complex, 35,000-50,000 AADT) Route 18 begins (at mile 5.1) at a partial-cloverleaf interchange with New Jersey Route 138 in Monmouth County. It heads northward as a four-lane freeway with several interchanges (with state and local roads), transversing several residential developments and areas of open space until reaching the Garden State Parkway (mile 14.3), a large interchange complex near the Naval Weapons Station Earle. Route 18's southern terminus (Source: Wikipedia) Sub-corridor 18B (Garden State Parkway to US Route 9, 16.1 miles, predominantly 65 MPH speed limit, with 55 MPH in the vicinity of the GSP interchange complex, 40,000-50,000 AADT) The route continues northwestward as a four lane freeway, transversing more residential developments, golf courses and wooded land for several miles. After entering Middlesex County, Route 18 continues north as a freeway. Sub-corridor 18B ends shortly thereafter, at the US Route 9 interchange (mile 30.4). NEW JERSEY PILOT STUDY 11

20 Sub-corridor 18C (US Route 9 to the NJ Turnpike, 9.5 miles, variable speed limits, from 40 MPH to 55 MPH, 40,000-80,000 AADT) After interchanging with U.S. Route 9 (exit 30), the freeway ends, and the route becomes an arterial highway through a mostly wooded commercial stretch of Old Bridge. The route crosses several roads in this area, as a four-lane divided hghway, until it reaches the County 527 interchange, where the road widens to 8 lanes for a short stretch (to the next interchange at County 615) then to six lanes. Signalized section of NJ 18 in East Brunswick (Source: Google Street View) Route 18 then continues through dense residential and heavily developed commercial development of East Brunswick as the road crosses over, then interchanges with the New Jersey Turnpike at exit 9 of the turnpike. Sub-corridor 18D (NJ Turnpike to Hoes Lane, 5.4 miles in length, variable speed limits, from 40 MPH to 55 MPH, 40, ,000 AADT) Following the NJ Turnpike interchange, Route 18 passes a large commercial complex, then the U.S. Route 1 interchange, and then separates into a local/express configuration (2 and 4 lanes, respectively), paralleling the Raritan River. After passing New Brunswick (with local exits serving downtown), the express and local lanes merge back together and cross under the New Jersey Transit Northeast Corridor Line train viaduct. The freeway continues as four lanes with exits for local streets serving Rutgers University s College Avenue Campus and Easton Avenue before exiting New Brunswick on the John A. Lynch, Sr. Memorial Bridge over the Raritan River. View along Route 18 southbound at the intersection with Tabernacle Way in New Brunswick (Source: Wikipedia) The highway then has a few interchanges (notably serving Rutgers' Busch and Livingston campuses and stadium), before ending near Buckingham Drive. The roadway continues as Hoes Lane, which heads north to an intersection with Centennial Avenue. 12 NEW JERSEY PILOT STUDY

21 Data: Sources, Gathering & Processing Data availability, suitability, and ease of use are critical issues for MPOs. The NJ Pilot Study used a variety of tools and data sources to get the needed analytical results. We gathered data for the pilot study from several sources, including: technical toolbox The I-95 Corridor Coalition s Vehicle Probe Project (VPP) Suite (INRIX data of archived 5-minute and hourly travel times by TMC); NJDOT s traffic count-based Congestion Management System (NJCMS) database (with estimates of average day hourly volumes by CMS link); New Jersey s Plan4Safety crash database (for drawing estimates of vehicle occupancy by sub-corridor); NJDOT s Weigh-in-Motion (WIM) database (hourly measurement of number of trucks and buses at selected station locations); and NJ TRANSIT s General Transit Feed Specification (GTFS) files (used to estimate number of NJ TRANSIT buses traveling specific sections of roadway by hour). Vehicle Probe Project Suite NJ Congestion Management System NJ-WIM W E I G H I N M O T I O N General Transit Feed Specification Based on data availability, we chose calendar year 2012 as the analysis time period. To our knowledge, there were no major transportation projects conducted on these two pilot corridors during this time period 11. Further, although Superstorm Sandy occurred during 2012, we do not think that we see any significant impact of the storm on the overall annual results. The I-95 Vehicle Probe Project, a collaborative effort among the Coalition, University of Maryland, and INRIX, has been providing comprehensive and continuous realtime travel information for more than six years. (Ideally, we would have liked to analyze multiple years, to ascertain whether there are any trends in the various performance measures values (and whether these trends are different for the different methods of calculating the measures), and to ascertain whether we can see the impacts of particular transportation or land use projects. Time did not allow for that in this effort, but we strongly believe that a multi-year analysis is critical to a fuller understanding of the impact of the choice of performance measures.) 11 Note that NJDOT performed a major reconstruction of both the local and express lanes of I-78 between NJ 24 and the Garden State Parkway (from 2005 through 2008), and opened new ramps connecting the Garden State Parkway (GSP) northbound to I- 78 westbound (September 2009) and the GSP southbound to I-78 eastbound (December 2010). The NJ Turnpike Authority replaced the deck on the Newark Bay Bridge from 2010 through The most recent significant project on NJ 18 involved the construction of express and local lanes through New Brunswick, from May 2005 to May NEW JERSEY PILOT STUDY 13

22 Detailed steps on data downloading and processing the archived travel time data can be found in Appendix C. However, below is a summary of the steps that we took: Obtained hourly and 5-minute average travel times from the VPP Suite Massive Raw Data Downloader for the entire calendar year 2012 Calculated average travel times by day of week and hour of day over entire year (e.g., average 8:00-8:59AM Mondays, average 8:00-8:59AM Tuesdays, etc.) to create synthetic average week Obtained hourly vehicle volumes from the NJCMS (manually conflated to the corridor s TMCs see Appendix D) Obtained hourly truck volumes from Weigh-in-Motion (WIM) data (see Appendix E) Obtained hourly bus volumes from NJ TRANSIT GTFS files (see Appendix F) Obtained three years of crash data from Plan4Safety, to compute average vehicle occupancy for each sub-corridor (see Appendix G) As we moved through the data gathering and processing effort, there were several considerations and issues worthy of note: Definition of average week The concept of an average week comes from the AASHTO recommended method, but they did not specify how to calculate the hourly averages. We considered two possibilities: Calculate a synthetic average week from averaging days of week over 52 weeks in a year (e.g., an average Sunday, average Monday, etc.). Select a specific week that approximates an average week. However, we would need to be careful to select a week that avoids major/minor holidays, school vacations, special events, weather events, etc. In addition to those issues, using this method would still cause a concern about whether that same week would be average from year to year. For this study, we calculated a synthetic week by averaging hourly travel times for each day of the week over the entire year (e.g., 12:00AM on Sundays, 1:00AM on Sundays, 2:00AM on Sundays, and so on through 11:00PM on Saturdays). Limited traffic volume granularity The hourly traffic volumes available from the NJCMS database are for a typical weekday. Although we could have estimated factors for each day of the week, instead we decided to take the simplistic approach of using the hourly volumes for each day of the week (including weekends). On first glance, this may likely result in an overestimate of delay. However, if there is delay on weekends, it may not occur during the same time of day that delay occurs on weekdays (typically morning and evening rush periods). Thus, delay may in fact be underestimated on weekends (because delay is typically associated with higher volumes, which likely occur at different times of the day on weekends than on weekdays). Having average hourly volumes by day of week (or even weekday versus weekend) would help address this problem. Express/local traffic volumes The NJCMS database does not provide separate volumes for express and local lanes of I-78, whereas INRIX provides distinct travel times for these TMC links. 14 NEW JERSEY PILOT STUDY

23 This issue will likely be solved in upcoming releases of NJCMS, where volumes will be reported separately for local and express lanes. For this effort, we decided to use the number of lanes (3 in local and 2 in express) to divide the NJCMS volume (3/5 of the total volume was assigned to the local lanes and 2/5 was assigned to the express lanes). Conflation of TMC and NJCMS networks Conflation (the process of combining information from two different geographic sources) was a time-consuming process, but was necessary because the travel time information was available for TMC segments, while the volume data was available for a differently segmented network (NJCMS links). For the method that we used in this project, please see Appendix D. The result of the conflation process was information on each TMC segment about the SRI (standard route identifier), and beginning and ending mileposts. We then used the milepost information for each TMC, along with milepost information for the same SRI available for each NJCMS link, to calculate an average volume (for each hour of the typical weekday) for each TMC (weighted by segment length). The end result of the data gathering and processing was a relational database containing all of the elements needed to calculate the delay and reliability performance measures: travel times, traffic volumes, vehicle occupancies, and transit ridership. Travel Time Thresholds: Defining and Evaluating The SCOPM findings document recommends that agencies should specify threshold travel times/speeds based on established agency practices and defensible factors. They list the following possible factors for consideration 12 : Corridor characteristics Local conditions; operational factors Community opinion about the desirability of additional capacity in a corridor; existing capacity Population growth Rural/urban routes Level of existing revenues Potential investment required to achieve performance levels We considered a range of possible thresholds as part of this pilot effort, using several thresholds that have validity, and that are used in different parts of the country. Each threshold option yields different results for the various performance measures, and each comes with a different set of policy implications. Note that for this study, we chose to cap all threshold travel times with the travel time at the speed limit for each link. For example, if the free-flow travel time on a link was shorter than the travel time needed to traverse that link at the posted speed limit, we used the speed limit travel time instead of the actual free-flow travel time. We did this as a policy to ensure that threshold (which people could interpret as a 12 Page 24 of Task Force Findings on National Level Measures FINAL ( ).pdf NEW JERSEY PILOT STUDY 15

24 Travel time, min target ) travel times did not require speeds in excess of the posted speed limit. In addition, in the case of a threshold for measuring delay, it ensures that we do not consider travel at (or above) the speed limit as delay. The graph below presents the average week hourly travel time data for Example Link A 13, along with the various thresholds. Specifically when considering delay measures, wherever the average travel time (dotted blue line) is above one of the threshold travel times (solid lines), that difference is counted towards delay Sample Thresholds Example Link A Average TT Freeflow TT Median TT, all hours and days Maximum Thruput TT "Acceptable" TT Median TT, by day and hr Sun 12 AM Mon 12 AM Tue 12 AM Wed 12 AM Thu 12 AM Fri 12 AM Sat 12 AM Sun 12 AM Day of week and hour of day Free-flow Travel Time Perhaps the most simplistic and most commonly used threshold is the amount of time necessary to travel a segment in free flowing conditions. In practice, this is often operationalized as the 15 th percentile travel time (i.e., the travel time at the 85 th percentile speed). As discussed above, for this study we used the travel time on a segment at the speed limit as a floor for the free-flow travel time (i.e., we 13 See page 23 for a description of Example Link A. 16 NEW JERSEY PILOT STUDY

25 increased any free-flow travel times that indicated speeds above the speed limit to represent the travel time at the speed limit) 14. In the graph for Example Link A above, the free-flow threshold travel time is shown as a straight red line near the bottom of the graph (travel time of 1.51 minutes 15 ). Using free-flow travel times as a threshold implies that freely flowing roadways are the goal at all times. This may not be an appropriate standard for transportation planning (and is not considered appropriate at the NJTPA), because: it does not maximize use of the roadway (see discussion of maximum throughput below), an open highway does not necessarily translate to better economic performance, and achievement of that goal may not be practical. In particular, using free-flow travel times for delay measures means that any travel at speeds less than free-flow speeds is considered delay. If the intent is to minimize, or even attempt to eliminate, delay, then it may not be appropriate to count all travel in conditions worse than free-flow in our measurement of delay. Similarly, using free-flow as a reliability threshold means that freeflow travel times are the benchmark on which to gauge reliability. In particular, for reliability measures that use the threshold travel time in the denominator of the measure, this means that the index value is to be multiplied by the free-flow travel time. Should we gauge RELIABILITY: AUTO VS. TRANSIT When considering reliability performance measures for highways, we find it useful to draw an analogy to the transit realm. When reporting transit on-time performance statistics, travel times are compared to scheduled times, which usually take into account expected congestion. When reporting on highway travel time reliability, we should use the same standards. Thus, when travel times are expected to be longer, for example during peak travel periods, this expected delay should be considered as part of the denominator of the measure (i.e., the threshold ). on-time performance (which is what reliability measures are attempting to measure) against free-flow travel times, or against more typical, expected travel times? We argue that it should be the latter. On the other hand, using anything other than free flow as a performance measure threshold can be problematic to communicate to the public. As WSDOT points out, the public has the default assumption that they should be able to travel at (or above) the speed limit: 14 Note that, while the INRIX data set does include a reference speed attribute that purports to be the free-flow speed, we instead calculated the 15 th percentile travel time from the data we downloaded and used that as the free-flow travel time. See Appendix C for a discussion of discrepancies that we found with the INRIX reference speed. 15 This travel time is actually the travel time at the 65 mph speed limit for this link, because the calculated 15 th percentile travel time for this link was 1.48 minutes corresponding to speeds faster than the speed limit NEW JERSEY PILOT STUDY 17

26 While managing the system to maximize and fully leverage existing public infrastructure investments seems a given, how can these principles be effectively and compellingly communicated to the public and decision makers? This is particularly difficult because the public is asked to accept congestion thresholds and strategies that do not manage the system to free flow or posted speeds - a perceived standard. (WSDOT, July ). Similarly, Texas A&M Transportation Institute (TTI) has recommended that free-flow be used as a threshold because it is easy to communicate and can be consistently applied across jurisdictions 17. TTI suggests that policy makers can use the target-setting process to identify which part of the delay in excess of free-flow to address with various transportation projects. While this is certainly a valid point, we might counter that this still adds a potentially confusing layer of complexity. It may be difficult to explain to the public why we, as planners and decision-makers, are measuring delay that we do not expect to reduce or eliminate. Thus, it may be better to consider one of the other possible thresholds discussed in the following sections, and we need to be vigilant to communicate the benefits of using thresholds that reflect conditions other than free flow. Median Travel Time over All Hours and Days Using the median travel time over all days as a threshold travel time means that the median (i.e., expected ) travel time (regardless of day of week or time of day) is the benchmark. This may be an appropriate benchmark when measuring delay, but may not be an appropriate threshold for reliability measures. One could argue that people s perception of delay is less tied to the actual time of day than their perceptions of reliability. However, because of the formulation of SCOPM s RI 80 measure, the threshold travel time needs to have one value for each link (i.e., it cannot vary over the course of a day). Thus, the only way to use a median travel time as a threshold is to use a median travel time calculated over a long period of time. In this pilot effort, we used the median travel time over all hours of all days 18. We calculated a single median travel time threshold from the 50 th percentile of the hourly travel time data for each TMC over the entire year. In the graph for Example Link A above, this median threshold travel time is shown as a straight green line, second from the bottom (travel time of 1.55 minutes). Median Travel Time by Day of Week and Hour of Day In addition to a single median travel time, we wanted to further refine the concept of median travel time by calculating the median travel time for each hour of the day for each day of the week. As discussed above, using a median travel time that varies by time of day and day of week may be more appropriate to use in measuring reliability than measuring delay One could potentially use the median travel time only during peak periods, but we did not evaluate that possibility in our study. 18 NEW JERSEY PILOT STUDY

27 To calculate this median travel time by day and hour, we used the hourly travel time data to calculate a 50 th percentile travel time for each hour of day and day of week. In the graph for Example Link A above, this threshold travel time appears as a thin orange line that tracks below the average travel time. Note that for this link, the median travel time during the Monday evening peak is much lower than the median travel times for the evening peaks on Tuesday through Thursday. Maximum Throughput Travel Time The idea of maximizing throughput (as opposed to reducing all delay) has been a focus for many transportation professionals, but has been especially prominent at the Washington State DOT (WSDOT). WSDOT uses maximum throughput as the basis for congestion performance measurement. As stated in WSDOT s Congestion Measurement Approach: Evaluating System Performance, From the perspective of operating the highway system as efficiently as possible, speeds at which the most vehicles can move through a highway segment (maximum throughput) is more meaningful than posted speed as the basis for measurement (WSDOT, June ). Because each roadway operates slightly differently, ideally we would conduct studies to determine the maximum throughput speed for each roadway segment. However, that is not practical. Based on their research, WSDOT has found that most roadways exhibit maximum throughput at between 70 and 85 percent of the posted speed limit, and they use 85% of the posted speed as a surrogate for the maximum throughput speed for the purpose of performance analysis. Thus, in this pilot study, we replicated WSDOT s practice and used 118% of the travel time at the speed limit (equivalent to 85% of the posted speed) to represent maximum throughput conditions 20. In the graph for Example Link A above, the maximum throughput travel time threshold is shown as a purple line (travel time of 1.78 minutes). Acceptable Travel Time The idea of setting an acceptable travel time based on the context of the roadway and the time of day is one that NJTPA has used in the past. The idea is that people will expect and accept slower speeds (longer travel times) as the surroundings get more urbanized, and also during the peak One issue that was noted during this project was that, because this threshold is based on posted speed, there are a few instances of this being an inappropriate threshold value. For example, on the links on I-78 approaching and leaving the NJ Turnpike toll plaza, the posted speed (according to NJDOT s straight line diagrams) is 65 mph. However, vehicles are slowing down and accelerating on these links, and thus cannot achieve anything near this posted speed, or even 85% of it. We did not address this issue during this study, but it warrants further investigation if maximum throughput is determined to be an appropriate threshold. NEW JERSEY PILOT STUDY 19

28 The Capital Area Metropolitan Planning Organization (CAMPO), in Austin, Texas, has also used variable speed thresholds based on area type (but not time of day), tolerating lower speeds in a central business district location than in a rural area 21. Area Type CAMPO s Established Speed Thresholds (miles per hour) Freeway Mainline Freeway HOV 22 Major Arterial Bus On Street Rail In Street Central Business District (CBD) CBD Fringe/Urban Residential Suburban Rural Bicycle As can be seen from the above table, CAMPO has used speed thresholds that range from 32 to 55 mph for freeways (assuming a 65 mph speed limit, this equates to between 50 and 85 percent of the speed limit). To operationalize this idea of using an acceptable speed as a travel time threshold, first we assigned each TMC an area type designation 23. Next, a matrix was developed that varies the threshold percent of free-flow travel time by both area type and time of day (peak vs. off-peak). In this pilot effort, we used the following values for percent of free-flow speed (converted to percent of free-flow travel time) based on previous NJTPA studies: Pilot Study Acceptable Speed/Travel Time Percentages Percent of Free-flow Speed Percent of Free-flow Travel Time Area Type Peak Off-peak Peak Off-peak Urban 60% 75% 167% 133% Suburban 75% 85% 133% 118% Rural 90% 95% 111% 105% We based these values on those that had been previously used at the NJTPA, but they could warrant further examination. As can be seen from the table above, the threshold speeds decrease (threshold travel times increase) as the surrounding land use becomes more urbanized, and when travel is during the High Occupancy Vehicle lanes 23 An initial area type was based on the area type assigned in NJTPA s regional travel demand model (NJRTM-E), but these assignments were modified somewhat because the NJRTM-E classifies some links on I-78 near Clinton as urban ; for the purposes of this study, we redesignated those links as suburban. 20 NEW JERSEY PILOT STUDY

29 TESTING ACCEPTABLE TRAVEL TIME RATIOS FOR PILOT CORRIDORS During this pilot effort, we evaluated the ratio of actual travel times to free-flow travel times for the two pilot corridors, grouped by area type and time of day. These results are shown below. Percent of Free-flow Speed Percent of Free-flow Travel Time Area Type Peak Off-peak Peak Off-peak Urban 82% 94% 122% 107% Suburban 94% 96% 107% 105% Rural 96% 96% 104% 105% Thus, the observed values do generally confirm both assumptions about acceptable travel times: 1) that travel times increase in peak periods, and 2) that travel times increase as the roadway travels through more urban areas. Note, however, that the table above lists peak but really combines both morning and evening peak periods, regardless of direction (i.e., even if the congestion is only in the morning peak in one direction and the evening peak in the opposite direction, the above table includes the travel time in both peaks in both directions). Singling out peak directions link by link for such a comparison would yield larger ratios for travel times (smaller ratios for speeds). peak period. Again, this table is merely a starting point for potential further discussion on acceptable travel times/speeds. In the graph for Example Link A above, the acceptable travel time threshold appears as a light green saw-tooth curve (alternating between a travel time of 1.97 minutes during off-peak periods, and 2.47 minutes during peak periods). This TMC is classified as urban, and thus these travel times represent 133% and 167%, respectively, of the free-flow travel time of 1.48 minutes). Threshold Consistency Across Agencies and Between Measures There are both benefits and drawbacks to thresholds specified by each agency, and that could differ from agency to agency. The major benefit is that each agency can tailor the performance measure to meet their particular goals and focus areas. For example, one agency might want to concentrate on maximizing throughput, while another agency might want to strive for freely flowing roadways. The major drawback to allowing agencies to set their own threshold is that measures calculated using differing thresholds are not comparable across agencies. This could cause confusion to the public by leading to apples to oranges comparisons, and could be problematic to FHWA if they want to roll up data from individual states into a national total. NEW JERSEY PILOT STUDY 21

30 SCOPM also argues that using one threshold for both delay and reliability performance measures makes communicating the results easier (especially to non-technical audiences). While this may be a valid argument, the ease of communication should be weighed against the appropriateness of using a particular threshold for either delay or reliability measures. This is because a threshold for reliability is inherently different from a threshold for delay 24. Due to the differing nature of what thresholds represent in delay versus what they represent in reliability, it may be appropriate to use different values for the two measures. 24 We could even argue that a different term is required for reliability, because the so-called threshold is more akin to a standard or expected condition, to which more extreme values of travel time are compared. This is in contrast to how thresholds are used more literally for delay calculations, where any travel in excess of that threshold is considered to be delay. 22 NEW JERSEY PILOT STUDY

31 Delay Performance Measures The first category of measures examined in this pilot study was delay. As a starting point, we used the AASHTO SCOPM proposed formulation for Annual Hours of Delay (AHD), considering various options for setting threshold travel times. In addition, we explored an alternate formulation for calculating delay. Annual Delay based on Average Week (SCOPM Formulation) As proposed, the SCOPM formula for AHD involves calculating the hours of delay during an average week and multiplying by 52 weeks per year. Specifically, we calculated delay for each day by comparing hourly travel times 25 to a predetermined threshold travel time. Delay occurs in hours where the reported travel time is greater than the threshold travel time. Equations to calculate AHD using the SCOPM formulation are as follows (Equations (1) and (2)): Delay hr of day,day of week = Vol hr of day max (0, (TT hr of day,day of week Threshold TT hr of day,day of week )) (1) 7 23 AHD = 52 ( ( (Delay hr,day of week ))) (2) day of week=1 hr of day=0 Note that the threshold travel time can be constant, or can vary by hour of day and/or day of week. The graph below shows an example graph of hourly average travel time for Example Link A (TMC , eastbound on I-78 local lanes, before exit 52 for the Garden State Parkway see Figure 3). TMC Figure 3: Example Link A 25 Alternatively, we could have used travel speeds instead of travel times. We chose to use travel times to make the calculations easier. NEW JERSEY PILOT STUDY 23

32 Travel time, min Sample "Synthetic" Average Week Example Link A Average travel time Sun 12 AM Mon 12 AM Tue 12 AM Wed 12 AM Thu 12 AM Fri 12 AM Sat 12 AM Sun 12 AM Day of week and hour of day Note that this link has longer travel times during both morning and evening peak periods, but had a different pattern for each day of the week. Monday has a worse morning peak, Tuesday has about the same travel times for morning and evening peaks, and Wednesday through Friday have worse evening peaks, although they seem to lessen as the week progresses. (As we discuss on page 38 in the section on an alternate calculation method that doesn t use an average week, we have concerns about whether calculating delay for an average week might underestimate delay, because it removes the extreme values from individual days.) Person Hours of Delay vs. Vehicle Hours of Delay Measuring delay in terms of person-hours instead of vehicle-hours helps maintain the focus of transportation on moving people and goods, not vehicles. Thus, it is helpful that the AHD measure can be calculated either on a person or vehicle basis. We used both average vehicle occupancy (applied to hourly vehicle volumes) and estimated hourly bus passenger volumes to obtain hourly person volumes on each TMC link. As can be seen from the tables below (for the maximum throughput travel time threshold 26 ), person-hours of delay without transit is very similar to vehicle-hours of delay, just scaled up by a vehicle occupancy factor 27. However, adding in the bus passenger volumes does make a substantial difference on those sub-corridors that have several buses (e.g., sub-corridor 78C, which carries more than 100 buses over the course of a day). Again, including transit volumes in the delay measure places emphasis on the delay that impacts the most number of people. 26 See page 26 for a discussion of the AHD results using all five threshold variations. 27 For the corridors examined in this study, vehicle occupancies did not vary much in the various sub-corridors, ranging from 1.27 to 1.36 on the NJ 18 corridor, and from 1.31 to 1.42 on the I-78 corridor. 24 NEW JERSEY PILOT STUDY

33 AHD Variation with Measurement Unit (Max. Throughput Threshold) Sub-corridor Miles Vehicle- Hours/mile AHD/mile by Measurement Unit Person- Hours/mile (w/o transit) Person- Hours/mile (w/ transit) Entire NJ 18 Corridor ,980 12,000 12,600 Northbound ,060 8,180 8,640 Southbound ,900 15,800 16,500 18A (both dir.) Northbound Southbound B (both dir.) Northbound Southbound C (both dir.) ,500 29,400 31,400 Northbound ,100 27,500 29,200 Southbound ,900 31,300 33,500 18D (both dir.) ,500 47,500 48,600 Northbound ,700 16,500 17,000 Southbound ,000 79,600 81,200 Entire I-78 Corridor ,000 18,100 19,100 Eastbound ,600 24,600 25,400 Westbound ,160 11,300 12,600 78A (both dir.) ,590 2,250 2,450 Eastbound ,170 1,660 1,790 Westbound ,990 2,820 3,090 78B (both dir.) ,960 2,570 2,770 Eastbound ,370 3,110 3,370 Westbound ,520 1,990 2,130 78C (both dir.) ,900 31,700 36,700 Eastbound ,100 13,900 16,400 Westbound ,000 48,400 55,800 78D (both dir.) , , ,000 Eastbound , , ,000 Westbound ,400 24,400 25,000 NEW JERSEY PILOT STUDY 25

34 AHD per mile (thousands) AHD Variation with Measurement Unit (Maximum Throughput Threshold) Measurement Unit Variations Vehicle-Hours Person-Hours (w/o transit) Person-Hours (w/ transit) NJ18 Corridor 18A 18B 18C 18D I-78 Corridor 78A 78B 78C 78D %&h(!"c$ Miles K I 26 NEW JERSEY PILOT STUDY

35 Truck and Bus Delay One of the MAP-21 national performance goal categories is improving freight movement and economic vitality, and one of the performance measures suggested by SCOPM for this goal category is annual hours of truck delay (AHTD), the travel time above the congestion threshold in units of vehicle-hours for Trucks on the Interstate Highway System. In this pilot study, we also wanted to measure delay specific to bus traffic as well. Thus, we calculated performance measures of annual hours of delay (AHD) for both trucks and buses. Incorporating Bus and Truck Volumes As discussed above, we were able to collect estimates of hourly truck volumes (by day of week) from the WIM data. We also compiled estimates of total hourly bus volumes (also by day of week) from both our analysis of the NJ TRANSIT GTFS files (for NJ TRANSIT bus volumes) and the WIM data (for private and university bus volumes). We then used those volumes in Equations (1) and (2) to obtain annual hours of delay for trucks and buses. Delay by Vehicle Type Depending on the spatial and temporal distribution of volumes and congestion, the results for delay can be quite different among the various vehicle times. For example, if trucks mostly operate on uncongested roadways and/or in uncongested time periods, truck delays can be relatively small. In addition, by showing where delay affects trucks and/or buses the most, we can target resources to highway segments that cause significant truck and/or bus delay. The following table and graph illustrate the difference, on a sub-corridor level, between total vehicle delay, truck delay, and bus delay. Note that, because the total volume of trucks is much lower than the volume of all vehicles (and the volume of buses is even lower still), delay for trucks and buses is shown on a different axis/scale than delay for all vehicles. You can see from these results those sub-corridors where delay impacts trucks (e.g., 18C and 78D) and buses (e.g., 78C) the most. NEW JERSEY PILOT STUDY 27

36 Sub-corridor AHD Variation with Vehicle Type (Max. Throughput Threshold) Miles AHD/mile by Vehicle Type All Vehicles Trucks Buses Entire NJ 18 Corridor , Northbound , Southbound , A (both dir.) Northbound Southbound B (both dir.) Northbound Southbound C (both dir.) ,500 1, Northbound , Southbound ,900 1, D (both dir.) , Northbound , Southbound , Entire I-78 Corridor , Eastbound ,600 1, Westbound , A (both dir.) , Eastbound , Westbound , B (both dir.) , Eastbound , Westbound , C (both dir.) ,900 1, Eastbound , Westbound ,000 2, D (both dir.) ,700 4, Eastbound ,000 6, Westbound ,400 1, NEW JERSEY PILOT STUDY

37 AHD per mile (All vehicles, thousands) AHD per mile (Trucks & Buses, thousands) AHD Variation with Vehicle Type (Max. Throughput Threshold) Vehicle Types All Vehicles Trucks Buses NJ18 Corridor 18A 18B 18C 18D I-78 Corridor 78A 78B 78C 78D 0 %&h(!"c$ Miles K I NEW JERSEY PILOT STUDY 29

38 Effects of Thresholds on Delay Values The following table presents the annual person-hours of delay calculated using each of the five thresholds discussed above. Sub-corridor AHD Variation with Thresholds (Person-Hours, with Transit) Miles Freeflow AHD/mile by Threshold Variations Yearly Median Day/Hr Median Max Throughput Acceptable Speed NJ 18 Corridor ,800 16,000 3,130 12,600 8,950 Northbound ,300 11,000 2,550 8,640 5,960 Southbound ,300 21,100 3,710 16,500 12,000 18A (both dir.) ,290 1, Northbound ,420 1, Southbound ,170 1, B (both dir.) Northbound Southbound C (both dir.) ,900 37,200 6,900 31,400 21,900 Northbound ,800 32,500 7,690 29,200 20,000 Southbound ,900 41,900 6,120 33,500 23,800 18D (both dir.) ,500 65,200 13,100 48,600 35,300 Northbound ,100 29,100 6,470 17,000 11,900 Southbound , ,000 20,000 81,200 59,500 I-78 Corridor ,200 21,800 11,300 19,100 9,480 Eastbound ,700 27,300 11,900 25,400 13,500 Westbound ,500 16,200 10,600 12,600 5,270 78A (both dir.) ,180 8,990 6,360 2,450 6,030 Eastbound ,250 8,970 6,290 1,790 5,340 Westbound ,110 9,010 6,430 3,090 6,700 78B (both dir.) ,080 7,580 6,080 2, Eastbound ,180 8,640 7,490 3, Westbound ,890 6,430 4,550 2,130 1,000 78C (both dir.) ,900 33,700 25,300 36,700 3,120 Eastbound ,100 21,100 18,000 16, Westbound ,700 45,600 32,200 55,800 5,720 78D (both dir.) ,000 94,100 26, ,000 54,400 Eastbound , ,000 34, ,000 85,300 Westbound ,800 36,200 17,500 25,000 13, NEW JERSEY PILOT STUDY

39 We can also represent the data in the above table as a graph (for clarity, the graph only shows each subcorridor in both directions, not in the individual directions). As can be seen in the graph, there is no consistent pattern among all five thresholds across all pilot sub-corridors. It is important to realize when examining the comparisons between the delay results between thresholds that, in general, a larger amount of delay implies that the threshold speed is higher (because a higher threshold speed means that more of the travel time at relatively lower speeds is counted toward delay). However, we can make some observations from these results, including: The free-flow threshold always results in the greatest amount of delay. The yearly median threshold always results in more delay than the median by hour and day. On the arterial NJ 18 sub-corridors (18C and 18D), the delay consistently decreases from freeflow, to yearly median, to maximum throughput, to acceptable speed, to day/hour median. This means that, on average, the threshold speeds decrease in the same fashion (i.e., the day/hour median is the lowest speed, while free-flow is the highest speed). This might be indicative of conditions where much of the delay is recurring delay (and thus the threshold speeds decrease in this predictable fashion). Delay using the maximum throughput threshold is only lower than delay using the acceptable speed threshold for the one predominantly rural sub-corridor, 78A (and is also lowest overall). This is because we used 85% of the posted speed to represent maximum throughput conditions, while the acceptable speed percentage was higher than 85% for rural roadways (90% for peak and 95% for off-peak). This might imply a need to fine-tune thresholds for specific planning contexts if they are being based on area types. There are a few I-78 sub-corridors where the acceptable threshold results in the lowest amount of delay (78B and 78C, both eastbound and westbound; and 78D westbound). Our working hypothesis is that this is because nearly all of the congestion on these completely urban sub-corridors occurs during the peak, when the multiplier that we used for acceptable speed was only 60% of the posted speed (equivalent to approximately 40 mph). However, the typical travel speeds on this sub-corridor are much higher, even during congested periods. This means that the other alternate threshold speeds (maximum throughput, the two medians, and freeflow) are higher than 40 mph, and capture more delay. NEW JERSEY PILOT STUDY 31

40 Annual Person-Hours of Delay per mile (thousands) AHD Variation with Thresholds (Person-Hours, with Transit) Threshold Variations Free-flow Yearly Median Max Throughput "Acceptable" Speed Day/Hr Median NJ18 Corridor 18A 18B 18C 18D I-78 Corridor 78A 78B 78C 78D In addition to the table and graph (which present summaries of the annual person-hours of delay per mile APHD/mile using each of the five threshold variations on a sub-corridor level), it is helpful to look at the delay results spatially, on a link level. Following are maps of APHD/mile for each threshold. Note that the category breakpoints were set to be consistent among the various maps to help visual comparison, but otherwise do not represent anything other than increasing levels of delay. The legends depict the minimum and maximum values for each map. The resulting ranges of delay that we obtained from using different thresholds indicates a need to engage in further discussions and critical thinking about the appropriate thresholds to use to measure delay. As we have discussed above, the decision about which threshold to use is primarily a policy-driven decision, based on what amount and type of delay can and/or should be addressed. 32 NEW JERSEY PILOT STUDY

41 Free-flow Threshold NEW JERSEY PILOT STUDY 33

42 Yearly Median Threshold 34 NEW JERSEY PILOT STUDY

43 Day/Hour Median Threshold NEW JERSEY PILOT STUDY 35

44 Maximum Throughput Threshold 36 NEW JERSEY PILOT STUDY

45 Acceptable Speed Threshold NEW JERSEY PILOT STUDY 37

46 Actual Annual Total Delay (Alternate Formulation) Calculation Method In addition to the SCOPM method, we tested an alternate method to calculate annual hours of delay. The rationale behind the alternate method was that SCOPM s use of an average week might underestimate actual annual delay; using actual hourly data may give a fuller, more accurate accounting of delay. Instead of using a synthetic average week, this alternate method calculates delay for all 8,760 hours in the year (24 hours/day x 365 days, or 8,784 hours in leap years like 2012). Volumes are taken from the corresponding hourly volume (ideally by day of week, but in this case, all days are assumed to have the same hourly volume pattern), and can be either vehicle volumes or person volumes. As with the SCOPM method, the threshold travel time can be either constant or based on the hour of day and day of week. This method can be seen in equation (3). 11PM Dec 31 AHD = (Vol hr of day max (0, (TT t Threshold TT hr of day,day of week ))) t= 12AM Jan 1 (3) Depending on the technique used for determining an average week, this alternate calculation method may require additional data 28. However, the technique used in this study (creating a synthetic average week ) already required an entire year s worth of hourly travel time data, so no additional data was required. In addition, a minor amount of additional computer processing is involved for this alternate calculation method (the Microsoft Access query takes a few minutes to complete). 28 Additional data will be required if a particular week was preselected to represent an average week instead of creating an average week from an entire year s worth of hourly data 38 NEW JERSEY PILOT STUDY

47 Effect of Formulation on Delay Values The table below shows the impact of using the alternate method to calculate AHD (using hourly data for the entire year instead of for an average week). AHD Variation with Formulation (Person-hours with transit, Max. Throughput Threshold) Sub-corridor Miles Formulation SCOPM Alternate Entire NJ 18 Corridor ,600 14,000 Northbound ,640 9,940 Southbound ,500 18,000 18A (both dir.) Northbound Southbound B (both dir.) Northbound Southbound C (both dir.) ,400 33,900 Northbound ,200 32,000 Southbound ,500 35,800 18D (both dir.) ,600 54,900 Northbound ,000 22,100 Southbound ,200 88,700 Entire I-78 Corridor ,100 24,600 Eastbound ,400 31,000 Westbound ,600 18,100 78A (both dir.) ,450 7,520 Eastbound ,790 7,290 Westbound ,090 7,730 78B (both dir.) ,770 7,380 Eastbound ,370 8,370 Westbound ,130 6,310 78C (both dir.) ,700 43,200 Eastbound ,400 23,200 Westbound ,800 61,900 78D (both dir.) , ,000 Eastbound , ,000 Westbound ,000 37,100 NEW JERSEY PILOT STUDY 39

48 Annual Person-Hours of Delay per mile (thousands) AHD Variation with Formulation (Person-hours with transit, Max. Throughput Threshold) Calculation Method SCOPM Alternate NJ18 Corridor 18A 18B 18C 18D I-78 Corridor 78A 78B 78C 78D As can be seen by the table and graph above, using the alternate method yields higher levels of delay. In some cases, it is substantially higher than using the SCOPM method. This difference is because we only count delay when travel times exceed the threshold. Many more individual hours exceed the threshold than the hours within the average week 29. It is interesting to note that the absolute magnitude of the difference between the two methods is fairly consistent between the sub-corridors in each of the pilot corridors. However, for sub-corridors that experience lesser amounts of delay (18A, 18B, 78A, and 78B), the percentage change between the two methods is quite dramatic. 29 If we counted instances where travel time was less than the threshold as a delay credit or negative delay, then these two methods would produce mathematically identical results. 40 NEW JERSEY PILOT STUDY

49 Reliability Performance Measures The second system performance measure that we examined in this pilot project is reliability. Specifically, the reliability performance measure proposed by SCOPM is termed a Reliability Index (also known as RI 80). As with the delay measure, we calculated this measure using the method suggested by SCOPM Task Force along with an alternate formulation, termed TTR Max (see below). And again, we tested several different thresholds in calculating these reliability performance measures. Inherent in each of these methods of calculating reliability measures is the ratio between 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 it to take? Reliability Index (SCOPM Formulation) SCOPM s Reliability Index (RI 80) is the ratio of the worst 80 th percentile travel time to an agency-determined threshold travel time. It can be thought of as the multiplier of travel time required to be on time RELIABILITY THRESHOLDS: CUSTOMER PERSPECTIVES In the selection of an appropriate threshold travel time for reliability measures, we should consider these measures from the perspective of a traveler/customer. When will a person consider a particular road segment as unreliable? If most people are comparing their worst travel time with free flow travel time, then we should use the free-flow travel time as the threshold travel time. Whereas, if most people are comparing their worst travel time with the time required to travel that road segment the majority of the time, then the yearly median travel time should be considered as a threshold. If we want the policy to be that roadways operate at their maximum efficiency, then the maximum throughput travel time might be the appropriate choice for the reliability threshold. For trips people make on a regular basis, they tend to know the travel time based on their everyday travel experience. Hence, for those trips they compare their worst travel time with some type of median travel time (presumably, a median for the time of day that they make their trip). However, for trips they make once in while such as leisure trips, they tend to compare the travel time with free flow travel time. Perhaps, people are more concerned about reaching those destinations on time where they go on a regular basis (such as job places) than to those they seldom visit. Hence comparing against a median travel time is a better option than comparing against free flow travel time. However, as discussed below, for trips that people make regularly, they most likely compare their travel time with what they typically experience at the time of day that they travel. Without modification, the SCOPM RI 80 formulation does not appear to allow that type of time-dependent threshold travel time. NEW JERSEY PILOT STUDY 41

50 80 percent of the time 30 (e.g., 4 days per workweek), if you happen to be traveling in the most congested five-minute period of the day (compared to a threshold travel time for that segment, regardless of time of day). The higher the index, the greater the amount of time required to reliably travel a road segment in relation to a threshold/expected travel time. Calculation method SCOPM specified the following method for calculating RI 80: Divide the day into 288 five-minute intervals (24 hours x 12 5-minute periods per hour = 288) For each five-minute interval (e.g., 8:00-8:05AM), array travel times from all days (either calendar days or workdays 31 ) in ascending order Calculate 80 th percentile travel time (TT 80) for each five-minute interval From the 288 values of TT 80, select highest value over the entire day (or just over the peak period) 32 RI 80 = Maximum TT 80 Threshold TT (4) Example Figure 4 shows the location for Example Link B (TMC 120P04419). This TMC is located on I-78 East, at the interchange of I- 78 and the NJ Turnpike/I-95 (at the beginning of the I-78 Newark Bay Extension). Due to the presence of tollbooths and ramps to the I-78 extension and the NJ Turnpike/I-95, vehicles do not move systematically along this TMC. It is typical for vehicles to change multiple lanes within a short distance and move from one side of Figure 4: Example Link B (Source: VPP Suite) TMC 120P04419 the road to the other side to travel on the correct ramp (see Google Street View image below). This gives rise to highly variable travel during certain times of the day and makes this TMC a good example to use to illustrate reliability performance measures. 30 There has been a great deal of discussion in the transportation planning community about whether to use the 80 th percentile (indicative of on-time performance 4 out of 5 days alternatively, allowing late arrival once per workweek) or the 95 th percentile (indicative of on-time performance 19 out of 20 days alternatively, allowing late arrival one workday per month). We have not addressed this debate in our pilot project, and have consistently used the 80 th percentile. 31 In this study, we used all calendar days, for ease of computation. 32 In this study, we selected the highest value over the entire day, although it typically occurred during the peak period. 42 NEW JERSEY PILOT STUDY

51 80th percentile travel time View of Example Link B (Source: Google Street View) Following the method described above, we arranged the reported travel times for this TMC segment during each fiveminute interval for the entire 2012 year in ascending order (i.e., 366 values 33 for each five-minute interval). Then we calculated the 80 th percentile travel time for each five-minute time interval from among the 366 days of travel times, leading to 288 values, as shown in the graph below TT80 TT 80 Example Link B Max TT 80 = 4.19 min (7:50 AM) Free-flow TT = 0.56 min Annual Median TT = 0.59 min Maximum Throughput TT = 0.52 min RI 80 = 7.5, 7.1, or :00 AM 12:00 PM 12:00 AM Time of the Day We then took the maximum value of TT 80 (4.19 minutes travel time required to travel the segment at 7:50 a.m., shown in the graph above), and divided that by a threshold travel time (free-flow, annual median, or maximum throughput travel time) to calculate the Reliability Index (7.5, 7.1, or 8.1 respectively). See below for a further discussion of how these RI 80 values correspond to each threshold was a leap year with 366 days. NEW JERSEY PILOT STUDY 43

52 Thresholds In this study, we used three types of threshold travel time to calculate the RI 80, as indicated in the graph above: the free-flow travel time (15 th percentile travel time), the yearly median travel time (50 th percentile travel time), and the maximum throughput travel time (travel time at 85% of the posted speed). 34 The three RI 80 values for Example Link B, using three different thresholds are as follows: RI 80 (Free-flow): MaxTT 80/Free-flow TT = 4.19 min/0.56 min = 7.48 RI 80 (Yearly median): MaxTT 80/Yearly Median TT = 4.19 min/0.59 min = 7.10 RI 80 (Max throughput): MaxTT 80/Maximum Throughput TT = 4.19min/0.52 min = As can be seen from the above calculations, the value of RI 80 varies with the threshold travel time. A higher threshold travel time (slower threshold speed) will lead to lower RI 80 values. Hence, the selection of a specific threshold travel time influences the value of the Reliability Index 36. It is important to note here that, because the formulation for RI 80 requires one value of the threshold for each TMC, it is not possible to use a time-dependent threshold (e.g., median travel time for a particular time of day), because the maximum 80 th percentile travel time occurs at different times of the day for each TMC. Effect of Threshold Variation on Reliability Index The following maps depict values of RI 80, using the three different threshold options, for TMC segments on I-78. For the majority of the TMC segments, the RI 80 values are lower when we use the maximum throughput travel time as the threshold travel time (because the maximum throughput travel time tends to be longer than the other two threshold travel time options). 34 See the discussion of Thresholds (starting on page 15) for more information on each threshold. 35 The maximum throughput travel time for this TMC reported above (0.52 minutes), is based on a speed limit in the NJDOT Straight Line Diagram of 65 mph. However, as discussed above, this TMC is immediately after a toll barrier, and thus it is unreasonable to expect that vehicles would be traveling at any speeds approaching this maximum speed limit. 36 A lower RI 80 value may be advantageous for initial reporting purposes (because it implies better baseline performance) but it may be difficult to improve through implementation of transportation projects in the long-term. 44 NEW JERSEY PILOT STUDY

53 Threshold: Free-flow TT B A A B RI80 Ranges Threshold: Max Throughput TT B A A B RI80 Ranges NEW JERSEY PILOT STUDY 45

54 Threshold: Yearly Median TT B A A B RI80 Ranges In the following table, we show the RI 80 values for four randomly selected TMC links (one from each subcorridor) on I-78. As can be seen from this table, the RI 80 values are lowest when the maximum throughput travel time is used as the threshold for the first two TMC links (on sub-corridors 78A and 78B). However, for the other two TMC links (on sub-corridors 78C and 78D) that is not the case. Hence, even though the RI 80 values are usually lower when maximum throughput travel time is the threshold, it is not always the case. Sub-Corridor TMC Codes Reliability Index Comparison RI 80 with Different Thresholds for Sample TMC Links on I-78 Free-flow TT threshold RI 80 Maximum throughput TT threshold 78A B C D 120P Yearly median TT threshold 46 NEW JERSEY PILOT STUDY

55 The following maps depict values of RI 80, using the three different threshold options, for TMC segments on NJ 18. As with I-78, for the majority of the TMC segments the RI 80 values are lower when the threshold is the maximum throughput travel time. Threshold: 15 th Percentile TT Threshold: Yearly Median TT Threshold: Max Throughput TT C C C C C C RI80 Ranges NEW JERSEY PILOT STUDY 47

56 The table below presents the RI 80 values for four randomly selected TMC links (one from each sub-corridor) on NJ 18. The values in this case strongly support the conclusion that the RI 80 values are lower with maximum throughput travel time as the threshold. Sub-Corridor TMC Codes Reliability Index Comparison RI 80 with Different Thresholds for Sample TMC Links on NJ 18 Free-flow TT threshold RI 80 Maximum throughput TT threshold Yearly median TT threshold 18A B C D The fact that Reliability Index is lower with the maximum throughput travel time as the threshold (compared to the other two threshold options), indicates that the maximum throughput travel time is higher than yearly median and free-flow travel times, which is typically the case. In other words, maximum throughput speed is lower than the yearly median and free-flow speeds. Dynamic Reliability Index (Alternate Formulation) For the Reliability Index, the SCOPM Task Force recommended a formula that compares the worst 80 th percentile travel time of the 288 five-minute intervals for the entire day with a single threshold travel time (not necessarily connected with the time period that has the worst 80 th percentile travel time). For instance, along Example Link B, the worst 80 th percentile travel time occurs at 7:50 a.m. in the morning (across all days in 2012). For the RI 80 calculation, we compare this travel time with a single threshold travel time (e.g., the free-flow travel time, or the median travel time) for the segment regardless of the time of day. This method does not incorporate any variation in the threshold ( expected ) travel time during the span of a day, or the connection between the time of day that the extreme travel time occurs and what the traveler would normally expect during that time period. For instance, if we are to assume that the median travel time represents the expected travel time for a traveler, then it is reasonable to assume that a reliability index should be the ratio between the extreme travel time (in this case, the worst 80 th percentile travel time) and the median travel time during that same 5 minute time period. However, the SCOPM formulation does not allow for that calculation, as the threshold travel time is determined a priori, or without the knowledge of when the worst 80 th percentile travel time occurs. As a result, the SCOPM Reliability Index compares the worst 80 th percentile travel time with a threshold travel time that may not usually correspond to that time of the day. Hence, we are suggesting in this 48 NEW JERSEY PILOT STUDY

57 study an alternate dynamic (i.e., variable by time of day) Reliability Index formulation (TTR Max) that accounts for this. Calculation method Our suggested method for calculating TTR Max is as follows: For each of the 288 five-minute intervals, across the entire year, calculate the 80th percentile travel time and threshold (median) travel time (either over all calendar days or just workdays), TT 80 and TT For each five-minute period, calculate Travel Time Ratio (TTR), using the 80th percentile travel time and the median travel time corresponding to it: TTR = TT 80 TT50 (5) Array values of TTR for entire day (or just peak period) from all days (either calendar days or workdays) in ascending order Select the maximum value of TTR (TTR Max): TTR Max = max(ttr) (6) In this study, we have calculated a reliability measure based on the above alternate formula (TTR Max) for both I-78 and NJ 18. Example For a better understanding of the formula, following is an example of the calculation of the reliability measure according to the alternate formula for Example Link B (the same TMC that was used in the RI 80 example). The first graph below shows the median (50 th percentile) and 80 th percentile travel times for this TMC segment, calculated for each five-minute interval (e.g., 8:00 a.m. to 8:05 a.m.), over all 366 days in The next graph shows the Travel Time Ratio (TTR) calculated for each five-minute interval throughout the day. TTR is the ratio between the 80 th percentile travel time and the 50 th percentile (median) travel time. As shown on the graph below, the maximum TTR value (TTR Max) is 4.8, and occurs at 8:55 a.m. This value is lower (and more realistic) than the RI 80 values (7.1, 7.5, or 8.1) calculated for this TMC. Of note, in this formulation it is possible to identify the time period during which the road segment is most unreliable (e.g., 8.55 a.m. on Example Link B). Note that this most unreliable time period is not the same as the time period that had the maximum 80 th percentile travel time (7:50 a.m.). This is because the median travel time at 7:50 a.m. is also relatively high, so the travel time ratio is not the highest. 37 In this study, we have used median (50 th percentile) travel time as the denominator (threshold) in the TTR Max calculation method. However, other thresholds such as a time-dependent free-flow (15 th percentile) travel time can also be used. However, if the threshold does not vary with time, the result will be identical to the result of using that threshold in the RI 80 calculation. NEW JERSEY PILOT STUDY 49

58 TT80/TT50 Travel Time, minutes Thus, for this example TMC, the most unreliable travel does not occur when the travel times are highest, but during the shoulder of the peak, when expected travel times are reducing but extreme values are still occurring frequently. This may be because volumes during the shoulder periods are much more variable than during the peak period. TTR Max Calculation Example Link B TT80 TT50 Max TT 80 = 4.19 min (7:50 AM) Max TT 50 = 1.73 min (7:55 AM) :00 AM 2:00 AM 4:00 AM 6:00 AM 8:00 AM 10:00 AM 12:00 PM 2:00 PM 4:00 PM 6:00 PM 8:00 PM 10:00 PM 12:00 AM Time of Day TTR TTR Max = 4.8 (8:55 AM) TT 80 = 3.32 min TT 50 = 0.69 min 3 2 Maximum unreliability is not at the same time as maximum 80 th percentile travel time :00 AM 2:00 AM 4:00 AM 6:00 AM 8:00 AM 10:00 AM 12:00 PM 2:00 PM 4:00 PM 6:00 PM 8:00 PM 10:00 PM 12:00 AM Time of Day Results The reliability performance measure values calculated through the alternate formula (TTR Max) with the median travel time as the threshold is the most comparable to the RI 80 values calculated with yearly median as the threshold. 50 NEW JERSEY PILOT STUDY

59 The reliability index values for unreliable segments of I-78 and NJ 18 are typically higher when calculated through the SCOPM recommended formula (RI 80) and lower when calculated through the alternate formula (TTR Max) as depicted in the map and table below. The map 38 shows the reliability values (using both methods) for all the TMC links of I-78 and NJ 18 and the table compares the reliability values of eight randomly selected segments (one from each sub-corridor of I-78 and NJ 18). Note that for TMC links with low reliability index values (more reliable segments), the RI 80 values are typically slightly lower than the TTR Max values. Thus, the TTR Max formulation appears to give more moderate values for reliability when compared to the RI 80 formulation. 38 Note that we selected the breakpoints for the ranges in the map to best illustrate the ranges of values from both calculation methods, and are the same across all maps, to ease in comparisons. NEW JERSEY PILOT STUDY 51

60 52 NEW JERSEY PILOT STUDY

61 Reliability Index Comparison RI 80 (yearly median threshold) vs. TTR Max for Sample TMC Links on I-78 and NJ 18 Sub- Corridor TMC Code RI 80 TTR Max 78A B C D 120P A B C D Aggregation of Reliability Values In this study, we calculated all performance measures (for delay and reliability) at the TMC (link) level. In order to report the performance at a sub-corridor, corridor, or region level, the TMC-level measures need to be combined/aggregated. However, aggregating TMCs up to sub-corridors and corridors is more problematic for the reliability measures than for the delay measures we explored. This is because the delay measures were in units of person- or vehicle-hours, which we can simply add together. The reliability performance measures, on the other hand, are index values (ratios), which cannot be directly added to aggregate up from the TMC level to sub-corridors and corridors. In this study, we looked at two primary methods for aggregating reliability measures: weighted averages and direct calculation. Method 1: Weighted Average of Reliability by Segment Calculation Method The first method for aggregating reliability performance measures that we examined was to take a weighted average of segment index values (Equation (7)). Weighted Avg for TMC 1 n = n i=1 (Weight i PM i ) n Weight (7) i We explored several weights as part of this project, as follows: i=1 Travel Time: Median Travel Time of the TMC link Length: Length of the TMC link in miles Vehicle Volume: Average daily vehicle-volume on the TMC link NEW JERSEY PILOT STUDY 53

62 Person Volume: Average daily person-volume on the TMC link (incorporates average vehicle occupancy for average daily vehicle-volume and weekday bus passenger volume) Vehicle Miles Travelled (VMT): Vehicle Volume TMC length Person Miles Travelled (PMT): Person Volume TMC length Vehicle Hours Travelled (VHT): Vehicle Volume Average Travel Time Person Hours Travelled (PHT): Person Volume Average Travel Time Each weight emphasizes certain specific links more than other links. Perhaps the most straightforward weight is travel time, because the reliability performance measures have travel time as the denominator. However, weighting by volume puts more emphasis on links that serve higher volumes, and weighting by person-volume places that weight on the number of persons traveling over that link. Weighting by person hours traveled (PHT) might best reflect planning priorities in accounting for impacts to travelers. Effects of Alternate Weighted Averaging on Reliability Index Values The following graphs provide the results of aggregating Reliability Index (RI 80) values to the sub-corridor and corridor levels. We calculated the aggregated RI 80 values presented below with free-flow travel time, maximum throughput travel time, and yearly median travel time as the threshold. The aggregated reliability values calculated through the alternate formula (TTR Max) are also presented below RI 80 Variation by Aggregation Weight (Free-flow Threshold) 2.60 Alternate Weights Travel Time Vehicle Vol VMT VHT Length Person Vol PMT PHT Corridor 78A 78B 78C 78D 18 Corridor 18A 18B 18C 18D %&h(!"c$ Miles K I 54 NEW JERSEY PILOT STUDY

63 RI 80 Variation by Aggregation Weight (Max. Throughput Threshold) Alternate Weights: 2.40 Travel Time Length Vehicle Vol VMT VHT Person Vol PMT PHT Corridor 78A 78B 78C 78D 18 Corridor 18A 18B 18C 18D Alternate Weights: RI 80 Variation by Aggregation Weight (Yearly Median Threshold) Travel Time Vehicle Vol VMT VHT Length Person Vol PMT PHT Corridor 78A 78B 78C 78D 18 Corridor 18A 18B 18C 18D The above graphs depict that RI 80 values vary as weights vary. For instance, the RI 80 values calculated with free-flow TT threshold vary from 2.36 to 2.72 for 78D and 2.33 to 2.63 for 18D. When calculated with maximum throughput as the threshold, RI 80 varies from 2.18 to 2.54 for 78D and 2.05 to 2.29 for 18D Values for each corridor and sub-corridor for each alternative weighting can be found in Appendix B. NEW JERSEY PILOT STUDY 55

64 However, the variation reduces and is not noticeable along sub-corridors where there is little congestion (and thus are very reliable) such as 78A and 78B, located in the rural and suburban parts of New Jersey and 18A and 18B where the roadway is limited access, and traffic volumes are lower than other parts of NJ 18. As noticed at the TMC link level, and at the aggregated sub-corridor level, RI 80 values vary with the thresholds. At the TMC link level, the RI 80 values were consistently low with maximum throughput as the threshold in comparison with the other thresholds. However, at the aggregated sub-corridor level this pattern does not appear. For instance, the 78A RI 80 values are lower when calculated with maximum throughput as the threshold in comparison to the other thresholds. However, for 78D the RI 80 values are lower when calculated with yearly median as the threshold in comparison to the other thresholds TTR Max Variation by Aggregation Weight Alternate Weights: Travel Time Vehicle Vol VMT VHT Length Person Vol PMT PHT Corridor 78A 78B 78C 78D 18 Corridor 18A 18B 18C 18D The above graph depicts aggregated reliability values using the alternate method suggested in this study (TTR Max). Because the TTR Max formula uses a median travel time in the denominator (i.e., as a threshold), it is probably best compared with the preceding graph showing RI 80 values with the yearly median as the threshold 40. The first thing to notice when comparing these graphs is that the range of TTR Max values is much smaller than the RI 80 values. This is not surprising, because the individual segment values for TTR Max were lower than the corresponding RI 80 values. 40 However, remember that the median threshold used in calculating RI 80 was the median over the entire year, for all time periods, while the median threshold used in calculating TTR Max was the median associated with the most unreliable 5-minute time period. 56 NEW JERSEY PILOT STUDY

65 INSTANTANEOUS VS. REALIZED TRAVEL TIMES It is important to point out that summing up the travel times within a corridor as was done in this direct aggregation method results in an instantaneous travel time for that corridor/sub-corridor, not the actual (or realized) travel time that a traveler would encounter as they travel that corridor over time. This is because the travel times experienced by a traveler as they travel along the corridor would necessarily come from different five-minute time intervals (assuming that it took more than five minutes to travel the length of the corridor). Depending on the length of the corridor, or the level of congestion, this difference between instantaneous and realized travel times may affect the value of the measure calculated. However, we note that values of some of the segments such as 78A, 78B, 18A, and 18B are similar and values of some of the other segments such as 78D are dissimilar. Hence, the reliability index values are not always noticeably lower, at the sub-corridor level, when calculated through the alternate formula as was depicted at the TMC link level before. Method 2: Direct Corridor/Sub-corridor Reliability Calculation Calculation Method In addition to computing weighted average reliability performance measures (e.g., RI 80, TTR Max) from individual TMC values, we can also directly calculate these measures at the corridor/sub-corridor level. This alternate aggregation method removes a possible overemphasis on segment-level travel time variability, because the typical user travels across multiple links to make his or her trip. However, this method does not weight reliability values by link-level volumes, and thus loses some sensitivity to the number of users experiencing localized travel time variability. In this method, we added up the travel times of all the TMC segments in a corridor/sub-corridor for each five-minute interval throughout the entire year, resulting in a travel time for each corridor and each subcorridor for each five-minute interval over the entire calendar year. We then calculated reliability performance measure values at the corridor and sub-corridor levels, following both the SCOPM method (RI 80) and the alternate formula offered in this study (TTR Max). Example The sub-corridor 78C extends from the Garden State Parkway (GSP) to the NJ Turnpike and is 5.4 miles long as shown in Figure 5 (left). This sub-corridor consists of multiple TMC segments (23 in the eastbound direction and 22 in the westbound direction). Figure 5 (right) shows a section of sub-corridor 78C with individual TMCs labeled. Note that this sub-corridor includes both express and local lanes of I-78. Here we simply added the travel times from the local and express lanes together In future, for more precise calculations, the travel times of the express and local lanes should calculated separately. NEW JERSEY PILOT STUDY 57

66 Travel Time (minutes) 78C 5.4 miles Miles I Figure 5: Sub-corridor 78-C We added up the travel times of all of these TMC links to get the sub-corridor travel time (in each direction) for each five-minute interval, for each day of the year. We calculated the 80 th percentile travel time (TT 80) and median travel time (TT 50) for all the 288 five minute intervals as shown in the graph below (for the 78C Eastbound sub-corridor). Note that the percentile value graphs are not smooth like the graph from the example individual TMC. This may relate to the fact that the travel times are instantaneous instead of realized (see sidebar) or the fact that this segment includes both express and local lanes th Percentile and Median Travel Times 78C Eastbound Sub-corridor AM 2 AM 4 AM 6 AM 8 AM 10 AM 12 PM 2 PM 4 PM 6 PM 8 PM 10 PM 12 AM Time of Day Percentiles across all days in 2014 TT80 TT50 For the RI 80 measure, we used the maximum value of TT 80 and divided that by the selected threshold travel times (which we calculated by adding up the individual TMC threshold travel times for each subcorridor and direction). For the TTR Max measure, we divided TT 80 by TT 50 for each 5-minute time interval and took the maximum value. The RI 80 values for this sub-corridor are 1.26 for eastbound traffic and 1.58 for westbound traffic. 58 NEW JERSEY PILOT STUDY

67 Effects of Alternate Aggregation Methods on Reliability Index Values Using the direct method for calculating reliability performance measures generally results in lower values than computing weighted averages of the individual TMC values. This may be due to (appropriate) de-emphasis on segment-by-segment time variability, focusing instead on variation along more meaningful lengths of travel. The graphs below show the results of the direct calculation method at the corridor and sub-corridor levels (by direction), compared with several of the weighted averages, for both the SCOPM Reliability Index (RI 80) and the alternative method proposed in this study (TTR Max). We note that, especially for the RI 80 formulation, the direct calculation method results in some values of 1.0 at the sub-corridor and corridor level (for 78B Eastbound, and for all NJ 18 sub-corridors except for 18C, and for the entire NJ 18 corridor as well). Remember that reliability index values of 1.0 indicate that there is no reliability problem on that entire corridor or sub-corridor. If there are individual segments within that sub-corridor that are unreliable, then having a measure that shows that the sub-corridor overall is reliable may be problematic. Using the direct calculation method for the TTR Max formulation does not result in aggregated values of 1.0 for any of the pilot corridors or sub-corridors, but the values for the most congested sub-corridors are substantially below the weighted average values. (This may suggest some benefit to the segmentlevel volume weighting approach.) Travel Time Person Vol PHT Direct RI 80 Variation by Aggregation Method (Max. Throughput Threshold) Alternate } Weights Corr EB 78 Corr WB 78A EB 78A WB 78B EB 78B WB 78C EB 78C WB 78D EB 78D WB 18 Corr NB 18 Corr SB 18A NB 18A SB 18B NB 18B SB 18C NB 18C SB 18D NB 18D SB NEW JERSEY PILOT STUDY 59

68 TTR Max Varation by Aggregation Method Travel Time Person Vol PHT Direct Alternate } Weights Corr EB 78 Corr WB 78A EB 78A WB 78B EB 78B WB 78C EB 78C WB 78D EB 78D WB 18 Corr NB 18 Corr SB 18A NB 18A SB 18B NB 18B SB 18C NB 18C SB 18D NB 18D SB Truck & Bus Reliability For the freight movement and economic vitality MAP-21 national performance goal category, one of the performance measures suggested by SCOPM is a Truck Reliability Index (Truck RI 80), which is the ratio of extreme (unexpected) truck travel time to expected truck travel time. This measure is similar to the RI 80 calculated above, with the difference being that truck volume is used as a weight to aggregate individual values of RI 80 calculated at the TMC level up to sub-corridors and corridors 42. In this pilot study, we have also calculated RI 80 values specific to bus travel. Thus, this section contains the methods and results associated with calculating RI 80 values specific to both trucks and buses. 42 Ideally, we would use travel time data for trucks only. Given the recent release of the National Performance Measure Research Dataset (NPMRDS), this may be possible in the future. 60 NEW JERSEY PILOT STUDY

69 Calculation Method As previously discussed, the Reliability Index values for all the TMC links were calculated based on the 80 th percentile travel time and three different threshold travel times (free-flow, yearly median, and maximum throughput). To calculate the RI 80 values specific to trucks and buses, we weighted the previously calculated RI 80 values by truck and bus volumes and aggregated them to the sub-corridor and corridor level. For the calculation, we used hourly truck volume estimates (by day of week) collected from Weigh-in-Motion (WIM) station data (see Figure 6). For buses, we added estimates of total hourly NJ TRANSIT bus volumes (also by day of week, from our analysis of the NJ TRANSIT GTFS files) to estimates of private/university buses (from the WIM station data). We then used those volumes were then used as a weight in Equation (1) to obtain Reliability Index values specific to trucks and buses. (Note: we did not calculate Reliability Index values specific to trucks and buses through the direct aggregation method because travel time data collected from the VPP Suite Massive Data Downloader did not provide travel time data specific to trucks and buses. Also, Figure 6: Weigh-in-Motion Sites as discussed above, we only used truck and bus volumes to weight individual TMC reliability index values in calculating measures for the sub-corridors and corridors.) Effect of Thresholds and Formulations on Bus and Truck Reliability Values The following graphs show the bus and truck Reliability Index values calculated applying different TT thresholds and different calculation methods. The Reliability Index values vary across the sub-corridors primarily based on the volume of bus and trucks traversing the corridor. For instance, the index values are high for 78D, for both trucks and buses, because this section of I-78 adjacent to New York has high volume of both trucks and buses in comparison to the other sections of the highway. On the other hand, the index values are high for 18D, primarily for buses, because several NJ TRANSIT buses traverse this corridor. The index values remain relatively low for trucks because this section of 18D is not as a prominent truck corridor as 78D. In comparison to Reliability Index values calculated previously for all types of vehicles, the reliability index values for buses and trucks are lower. This may be primarily due to the nature and magnitude of the weights. NEW JERSEY PILOT STUDY 61