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Cost-Effective Performance Measures for Travel Time Delay, Variation, and Reliability (2008)

Chapter: Chapter 8 - Using Travel Time Data in Planning and Decision Making

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Suggested Citation:"Chapter 8 - Using Travel Time Data in Planning and Decision Making." National Academies of Sciences, Engineering, and Medicine. 2008. Cost-Effective Performance Measures for Travel Time Delay, Variation, and Reliability. Washington, DC: The National Academies Press. doi: 10.17226/14167.
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Suggested Citation:"Chapter 8 - Using Travel Time Data in Planning and Decision Making." National Academies of Sciences, Engineering, and Medicine. 2008. Cost-Effective Performance Measures for Travel Time Delay, Variation, and Reliability. Washington, DC: The National Academies Press. doi: 10.17226/14167.
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Suggested Citation:"Chapter 8 - Using Travel Time Data in Planning and Decision Making." National Academies of Sciences, Engineering, and Medicine. 2008. Cost-Effective Performance Measures for Travel Time Delay, Variation, and Reliability. Washington, DC: The National Academies Press. doi: 10.17226/14167.
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Suggested Citation:"Chapter 8 - Using Travel Time Data in Planning and Decision Making." National Academies of Sciences, Engineering, and Medicine. 2008. Cost-Effective Performance Measures for Travel Time Delay, Variation, and Reliability. Washington, DC: The National Academies Press. doi: 10.17226/14167.
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Suggested Citation:"Chapter 8 - Using Travel Time Data in Planning and Decision Making." National Academies of Sciences, Engineering, and Medicine. 2008. Cost-Effective Performance Measures for Travel Time Delay, Variation, and Reliability. Washington, DC: The National Academies Press. doi: 10.17226/14167.
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Suggested Citation:"Chapter 8 - Using Travel Time Data in Planning and Decision Making." National Academies of Sciences, Engineering, and Medicine. 2008. Cost-Effective Performance Measures for Travel Time Delay, Variation, and Reliability. Washington, DC: The National Academies Press. doi: 10.17226/14167.
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Suggested Citation:"Chapter 8 - Using Travel Time Data in Planning and Decision Making." National Academies of Sciences, Engineering, and Medicine. 2008. Cost-Effective Performance Measures for Travel Time Delay, Variation, and Reliability. Washington, DC: The National Academies Press. doi: 10.17226/14167.
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Suggested Citation:"Chapter 8 - Using Travel Time Data in Planning and Decision Making." National Academies of Sciences, Engineering, and Medicine. 2008. Cost-Effective Performance Measures for Travel Time Delay, Variation, and Reliability. Washington, DC: The National Academies Press. doi: 10.17226/14167.
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Suggested Citation:"Chapter 8 - Using Travel Time Data in Planning and Decision Making." National Academies of Sciences, Engineering, and Medicine. 2008. Cost-Effective Performance Measures for Travel Time Delay, Variation, and Reliability. Washington, DC: The National Academies Press. doi: 10.17226/14167.
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Suggested Citation:"Chapter 8 - Using Travel Time Data in Planning and Decision Making." National Academies of Sciences, Engineering, and Medicine. 2008. Cost-Effective Performance Measures for Travel Time Delay, Variation, and Reliability. Washington, DC: The National Academies Press. doi: 10.17226/14167.
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Suggested Citation:"Chapter 8 - Using Travel Time Data in Planning and Decision Making." National Academies of Sciences, Engineering, and Medicine. 2008. Cost-Effective Performance Measures for Travel Time Delay, Variation, and Reliability. Washington, DC: The National Academies Press. doi: 10.17226/14167.
×
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Suggested Citation:"Chapter 8 - Using Travel Time Data in Planning and Decision Making." National Academies of Sciences, Engineering, and Medicine. 2008. Cost-Effective Performance Measures for Travel Time Delay, Variation, and Reliability. Washington, DC: The National Academies Press. doi: 10.17226/14167.
×
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Suggested Citation:"Chapter 8 - Using Travel Time Data in Planning and Decision Making." National Academies of Sciences, Engineering, and Medicine. 2008. Cost-Effective Performance Measures for Travel Time Delay, Variation, and Reliability. Washington, DC: The National Academies Press. doi: 10.17226/14167.
×
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Suggested Citation:"Chapter 8 - Using Travel Time Data in Planning and Decision Making." National Academies of Sciences, Engineering, and Medicine. 2008. Cost-Effective Performance Measures for Travel Time Delay, Variation, and Reliability. Washington, DC: The National Academies Press. doi: 10.17226/14167.
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55 8.1 Introduction This section provides guidance on how to utilize travel- time-based performance information in typical planning ap- plications faced by departments of transportation, regional planning agencies, transit system operators, and other agen- cies with similar roles and responsibilities. The selection and presentation of information in this chapter reflects the find- ing that a very large percentage of reported travel time, delay, and reliability data is used primarily for reporting on current conditions or historical trends. Many of the published or web-based sources of travel time data are used to inform the public, stakeholders, and decision makers about how well a system is currently performing and/or what has been the impact of a particular program of investment. Less evident is the application of similar types of data to drive typical plan- ning functions, such as current and projected future needs (or deficiencies) identification, comparison of alternatives, or hypothetical before/after (or “what if”) studies prior to actual project selection and implementation. This guidebook is intended to help fill that gap and provide practitioners with accessible, effective methods for bringing travel-time-based data into the decision process for potential future actions, as well as to identify and evaluate needs by looking at both historical and projected trends. 8.2 Scope and Limitations This material is primarily suited to support planning deci- sions about investment in system expansion, and to a lesser extent, on system operations. It focuses on using travel-time- related measures of system performance to discern looming trends, identify needs, and distinguish between alternative courses of actions. It is not intended to provide advice on short- term system management and detailed operational analyses based upon archived TMC data, nor on traveler information systems based on real-time data. There are other excellent sources of detailed information and practical guidance cover- ing that topic, including most recently NCHRP Project 3-68, A Guide to Effective Freeway Performance Measurement, which covers a wide range of material related to reporting conditions on freeway systems using TMC type data. 8.3 Organization The next several subsections provide context for applying travel time and related performance data to planning processes and decisions, drawing examples from the case study research. These findings illustrate some of the important technical and institutional steps or approaches that should be considered to improve the quality and utility of performance data in these applications. In the remainder of the chapter, we offer six different example planning applications frequently confronted by planning agencies, and offer step-by-step ap- proaches for applying the technical methods and approaches provided in the earlier chapters of this guide. 8.4 Creating a Performance-Based Decision-Making Environment Performance measures have been used to evaluate both system condition and quality, as well as to track the level of activity required to build, maintain, and operate a system. Planners talk of output or activity-based measures that quantify the level of effort that goes into the system, such as incident detection and clearance time, as well as outcome or quality of service measures that describe the resulting effect of investment choices (e.g., total annual delay per person). There is an even longer history of using performance data to track the physical condition of the transportation system, for example, in pavement and bridge management. The literature search, agency interviews, and case studies conducted during the course of this project suggest that use of travel time and delay data for planning purposes is currently C H A P T E R 8 Using Travel Time Data in Planning and Decision Making

56 limited. Most agencies do not actively use such data or projections in their planning processes. Much more common is the use of travel time, delay, and to some extent reliability statistics, for reporting current operational conditions and his- torical trends, and possibly identifying congested corridors for further analysis. What is still relatively new for most agencies is the use of the quality of service measures based on measured and modeled travel times, used in conjunction with other measures and factors, to help decision makers choose the most effective course of action. Actual ongoing application of such data to specific needs identification, alternatives analysis, or before and after studies (including the hypothetical before/ after or “what if” analysis comparing synthetic forecasts) is much more limited. Yet travel time and delay statistics can be useful in helping analysts, decision makers, and the general public to understand the potential payoff of different capital and operating investments in terms that are most immediately relevant to daily trip-making of system users. 8.4.1 Using Travel Time and Delay Measures Transportation project complexity and costs are continu- ing to grow significantly. The costs for design, materials, energy, construction, and environmental review and mitiga- tion, among other elements, are all escalating at rates higher than general inflation or transportation funds. Project costs of $100 million (and in some case much more) are no longer un- usual. Simultaneously, agency planners, decision makers, and even the lay public are increasingly aware of the important benefits that stem from good system investments, in terms of improved economic vitality, more efficient movement for personal and commercial purposes, and a resulting overall higher quality of life than would be present without the investment. In short, most stakeholders are looking for greater return on investment from transportation expenditures. Because travel time and delay affect this broader stream of benefits, there is a compelling case for including analysis of these factors in deciding on future investments. Identifying, selecting, and implementing the best performing projects, not simply the least expensive projects, are increasingly important as the cost of building, operating, and maintaining a modern transportation system grows, and its importance to the over- all well-being of the community grows as well. At the same time, members of the traveling public do not always understand why the large amount of ongoing trans- portation expenditures (which are visually evident to any system user, due to ever-present construction and mainte- nance) do not result in more significant improvements to the quality of their travel experience, regardless of mode. Finally, decision makers, elected or appointed officials, face increasing pressure to deliver quantifiable results. This phe- nomenon is not unique to transportation. It encompasses education (tracking test scores), welfare (tracking numbers of welfare recipients), environment (particles per million), and other disciplines in the public’s eye. Congestion mitigation (if not outright reduction) and mobility management are still high on the list of decision makers’ objectives, and even the concept of travel time reliability has worked its way into the regular di- alog of decision makers and even the general traveling public. Of course, the private sector has faced this type of ac- countability for decades. Publicly traded for-profit companies have to maximize shareholder value by producing financial re- sults that reflect profitability, revenue growth, positive cash flows, and other indicators believed to be central to a company’s mission and objectives. Even though these companies adopt and track other metrics for success (e.g., number of registered patents, effective knowledge management, retaining key staff, minimizing work-related accidents), ultimately they are judged by profitability and growth or, more generally speaking, return on investment. The same can be said for transportation. One could argue that even though many performance outcomes should be con- sidered in evaluating each project or investment (e.g., safety, environmental quality, social equity, geographic equity, etc.), travel time and reliability of travel time are the most important and immediate indicators of system performance and mobil- ity for most customers. People want to be able to get from point A to point B in a reasonable time with reasonable predictabil- ity. These two attributes should be among the key factors for any transportation planning process and for any decision- making process, in most cases. The selected performance measures should offer decision makers an understanding of the differences in travel time and reliability that would result from alternative courses of action. These can be aggregated to the region or system level, or reported individually for specific project corridors or segments. Our experience in working directly with numerous public agencies in performance-based planning and management suggests several important considerations. The case studies and agency interviews conducted for this research project support these findings and recommendations as well. 8.4.2 Make Performance Part of Everyone’s Daily Discussions Much has already been written about the institutional aspects of developing a successful performance-based man- agement approach. A frequently cited tactic is to raise the visibility of performance data and performance monitoring to the point that every division in the organization is engaged in some aspect of performance delivery, knows the relevant met- rics and desired targets or objectives, and is comfortable dis- cussing them. This is much easier said than done. To illustrate this point, ask yourself: how many people in the organization

57 On-Time Arrival (Within 5 Minutes of Average) Before 65% After Average Travel Time 22 minutes 18 minutes Buffer Index 30% 32% 65% Exhibit 8.1. Example before-after comparison of different travel time measures. know the average travel time (or total daily delay) in their re- gion (or state) and the reliability of travel time? How many know what the agency’s prediction for the next five years is for these two measures? What are the reasons for these predic- tions, and how are they tracking their progress? This means that every planning product, every presentation to decision makers, every staff recommendation for investment must be performance driven or at least include a discussion on performance impacts. Clearly, this takes time and effort. But as discussed before, performance-based planning and decision making have become an imperative, not a choice. Achieving the best results from an investment requires an up-front investment in planning analysis. 8.4.3 Develop an In-Depth Understanding of Trends and Measures For performance data to have an impact on agency deci- sions, there needs to be wide understanding within the organ- ization of the agency actions and external trends that are driving performance, the measures that are used to gauge per- formance, and the relationship between the two. Again, this seems easier than it really is. For instance, several agencies we have reviewed evaluate measures of mobility (e.g., travel time, delay, speed) independently from reliability (e.g., on-time ar- rival, percent variation of travel time, buffer index). Yet, these two measures are interdependent. If the number of accidents are reduced (by implementing safety projects) and/or accident clearance times are reduced (by investing in incident manage- ment strategies), planners and decision makers expect an im- provement in the reliability measure. Yet, in some instances that may not happen. Since delays due to accidents are re- duced, the average travel time over a month (or year) also will be reduced. This, in turn, changes what on-time arrival means, what the percent variation means, and what the buffer index (as a percent of travel time) refers to. Therefore, it is critical to look at the trends of both travel time and reliability together. The hypothetical example in Exhibit 8.1 illustrates this point. Clearly, the after scenario reflects an improvement over the before scenario, even if it does result in an increase in the variability as measured by the buffer index. Yet, unless the two types of measures are considered together, planners and decision makers may reach the wrong conclusion about the benefits of the project. Another example relates to the use of delay as a planning evaluation measure. In its 2004 Regional Transportation Plan, the Southern California Association of Governments (SCAG) projected future delays and compared them to the base year delay. At first, the results of this comparison were disappoint- ing. Despite a variety of potential system investments over 25 years costing more than $100 billion, total delay was projected to increase significantly between the base year and the horizon year of 2030. Yet, SCAG recognized that total system delay does not re- flect the individual customer’s experience, or their expecta- tions. Rather, delay per trip was deemed a more appropriate measure, since it is linked to something the traveler actually experiences and can measure on their own (even if only casu- ally or subconsciously) (i.e., the excess time required to make a particular trip due to congestion). In some cases, growth in delay is more meaningful to the traveler than growth in travel time, since travel time may be expected to increase due to land use policies, personal location decisions, etc. As shown in Exhibit 8.2, the two examples lead to different conclusions about the future system performance and the benefits of the improvement program. As these graphs show, delay per capita is projected to stay almost constant despite the increase in de- mand. To many transportation professionals, this projection, if it holds true, would be a major accomplishment. And to many system users, it also would seem a reasonable outcome, if taken from a realistic perspective of population growth and continued economic prosperity in the region. The point made here is that adopting and generating per- formance measures are not enough. An organization must spend significant time to understand the measure, the results, and the limitations of the measure before basing decisions on the measure. 8.4.4 Invest in Data Perhaps the seemingly most under-valued investment is the collection and storage of good monitoring data. The

58 2.2 5.2 3.0 0 1 2 3 4 5 6 D ai ly P er so n Ho ur s of D el ay (in m illi on s) Base Year 2000 Baseline 2030 Plan 2030 7.9 13.6 8.0 0 2 4 6 8 10 12 14 A vg . D ai ly D el ay p er C ap ita (in m ill io ns ) Base Year 2000 Baseline 2030 Plan 2030 Exhibit 8.2. Delay and delay per capita projected for 2030 in the SCAG region. number of planning studies conducted by state and local agencies that must rely only on existing, readily available data from secondary sources suggests as much. Yet, moni- toring data is used not only to measure what is. It is also used to calibrate the models that eventually project what will be. It is, therefore, important for both decision-makers and planning professionals to embrace the need for more frequent and regular data collection, and to view data and data systems as assets to be developed and valued. A review of the private sector confirms the importance that should be placed on data if decisions are to be based on performance. Wal-Mart has implemented systems that let them know what item is sold when, as well as the trends for each item on a daily and weekly basis. FedEx can tell where a shipment is at all times and can project when it will be delivered. And, of course, Internet companies can learn from online transac- tion and search trends to tailor advertisements for each in- dividual. Without good data, performance measurement cannot succeed as a basis for planning and decision making. Fortunately, developments in data collection equipment strongly suggest that automated detection systems are becoming more affordable and easier to install. Such systems are especially important to evaluate trends in travel time and reliability. Moreover, over the longer term, they are likely to prove more cost effective than manual data collection efforts. 8.4.5 Understand the Limitations of Tools and Continually Improve Them With the ever increasing computer power and the contin- uous advancement of the science of transportation and traf- fic engineering, it is important for agencies to understand the limitations of their current tools and, when possible, invest in improving them. For instance, 4-step travel demand models have limitations in terms of evaluating operational strategies (e.g., incident management, auxiliary lanes, ramp metering). Therefore, agencies that are about to focus on such strategies must look for alternative tools, such as mi- crosimulation tools. Moreover, as more data is available, travel demand models can be improved through better cali- bration. Again, we see this commitment to improving tools across the private sector. Financial firms, for instance, have abandoned many of the traditional stock and option valua- tion models over recent years as new data illustrated serious flaws in them. Car companies have developed new computer tools to help them assess wind resistance and the impact on fuel utilization. The list goes on, but the principle remains: if the tools are important for decision-making, improving the tools must be a priority. Research projects, such as these and many others like it, ultimately provide planners with the nec- essary tools to estimate and apply travel time performance data in a broad variety of situations. 8.4.6 Understand and Embrace the Difference Between Policy and Technical Analysis We have all witnessed the frustration of technical staff when decision makers do not allocate the suggested invest- ment to their area (e.g., pavement rehabilitation, highway expansion, operational strategies). This frustration is under- standable and perhaps even needed. After all, each program area needs advocacy. However, agency technical staff also must recognize that their primary job is to adequately inform decision makers of the performance ramifications of their po- tential decisions from a technical perspective (e.g., what are the cost ramifications in the future of deferring maintenance in order to address a critical capacity deficiency). This way, staff can focus on technical analysis, risk analysis, and per- formance measurement to provide an accurate picture as possible to decision makers. Using available tools and meth- ods to generate with and without estimates of volume, speed, travel time, and physical condition, analysts can generate measures that help identify the difference between these two choices.

59 8.4.7 Do Not Attempt to Use a Black Box Approach Sophisticated tools that build a prioritized list of improve- ment projects may appeal to technical staff, but more rarely are appreciated by decision makers and the public. It is criti- cal to work with the stakeholders during the evaluation process, and to explain the strengths and weaknesses of the tools during the entire planning process. Otherwise, the first time a credible source provides a negative critique of the tools used, decision makers may lose faith or withdraw support for the entire set of recommendations. Ultimately, integrating performance results into planning and decision making takes time. A review and revision/ enhancement of each tool and product may be required. But small steps can yield superior results that only can help en- force the overall commitment to the concept. 8.5 Using Travel Time, Delay, and Reliability in Planning Applications This section and the subsequent section summarize how the detailed methods and approaches described in Chapters 2 through 7 can be applied to typical transportation planning analysis in support of decisions. The material in Chapter 2 provides guidance for the selection of performance measures suitable for particular applications, and Chapter 3 provides data collection steps and actual equations for calculating the various measures. Chapters 4 through 7 describe specific analyses that can be performed using travel time and delay data. Exhibit 8.3 presents a recommended short list of measures for reporting travel time, delay, and reliability. These measures are organized according to whether they report primarily travel time, congestion-related delay, or reliability in planning applications. This table also indicates which component of congestion is reported, and the geographic area(s) best addressed by each measure. In fact, many measures can be applied at multiple scales (e.g., region, subarea, section, and corridor), which makes the measures useful for multiple applications (e.g., long-range planning), as well as corridor- specific alternatives analysis. There are numerous variants to the recommended meas- ures that may be useful depending upon the audience and application. For example, some measures may be expressed as an absolute number, as well as a percentage (e.g., percent or number of system lane miles operating at or below the defined threshold of congestion). The raw number of congested miles, in this example, may not give the lay person adequate sense of the magnitude of the problem, since they are unlikely to know the total extent of the system mileage. For that person it may be adequate to simply know that, for example, two-thirds of the system operate at acceptable levels under peak conditions, or that a proposed operational improvement covering a sig- nificant portion of the system (stepped-up freeway patrols, for example) might reduce congested miles by several percentage points. Conversely, for the decision-maker or elected official with budget concerns, knowing the absolute number of con- gested miles may be useful as it highlights more dramatically the extent of the problem and immediately conveys at least a gross sense of the size of undertaking and resources required to address the problem. Indexing a quantity (e.g., annual hours of delay) to some baseline quantity, such an area’s population or miles of travel may help to normalize the influence of background popula- tion growth when comparing current to future congestion levels, as demonstrated in the previous example from SCAG. This information may be more meaningful to agencies study- ing conditions at the corridor or regional level, for example, hours of delay per lane-mile of road, person hours of delay per 1,000 person miles traveled, or hours per 1,000 travelers. The general public may not gain much added benefit from these variant measures since it is more difficult to relate to personal travel decisions or travel experience to some of the indexed quantities. Again, the analyst should be guided by the primary audience for the performance data and choose accordingly. The con- cepts and calculations are similar regardless of the variant, and most analysts will be readily able to adapt measures to suit their particular needs. 8.6 Typical Planning Applications This section describes applications for measures of travel time, delay, and reliability in the planning process. Six typical planning applications were selected, based upon review of the research conducted for this project and the needs of practi- tioners as perceived by the research team and project panel: 1. Evaluate trends in travel time, delay, and reliability; 2. Identify existing deficiencies; 3. Evaluate the actual effectiveness of improvements (before- after study); 4. Predict future conditions/identify future needs and defi- ciencies; 5. Alternatives analysis; and 6. Improve fleet operations and productivity. These six applications address a very large percentage of the situations in which a planner or analyst might want to apply measures of travel time, delay, and reliability in order to shed more light on a trend or need, discern differences between alternative courses of action, etc. Each of these

60 Recommended Performance Measures Congestion Component Addressed Geographic Area Addressed Typical Units Reported Travel Time Measures Travel Time Duration Region Person-minutes/day, person-hours/year Total Travel Time Duration Region Person or vehicle hours of travel/year Accessibility Extent, Intensity Region, Subarea # or % of “opportunities” (e.g., jobs) where travel time < target travel time Delay and Congestion Measures Delay per Traveler Intensity Region, Subarea, Section, corridor Person-minutes/day, person-hours/year Total Delay Intensity Region, Subarea, Section, Corridor Person- or vehicle-hours of delay/year Travel Time Index or Travel Rate Index Intensity Region, Subarea, Section, Corridor Dimensionless factor that expresses ratio of travel conditions in the peak period to conditions during free-flow (e.g., TTI of 1.20 = congested trip is 20% longer than free-flow trip) Congested Travel Extent, Intensity Region, Subarea Vehicle-miles under congested conditions Percent of Congested Travel Duration, Extent, Intensity Region, Subarea Congested person-hours of travel (PHT) as % or ratio of total PHT Congested Roadway Extent, Intensity Region, Subarea # (or %) of miles of congested roadway Misery Index Duration, Intensity Region, Subarea, Corridor Proportion or percentage (e.g., 1.50) (expressing time difference between the average trip and the slowest 10 percent of trips) Reliability Measures Buffer Index Intensity, Variability Region, Subarea, Section, Corridor % extra time to be allowed to ensure on-time arrival, e.g., “BI of 30%” Percent On-Time Arrival Variability Facility, Corridor, System % of trips meeting definition of “on time” Planning Time Index Intensity, Variability Region, Subarea, Section, Corridor Dimensionless factor applied to normal trip time, e.g., PTI of 1.20 x 15-min. off-peak trip = 18-min. travel time for travel planning purposes Percent Variation Intensity, Variability Region, Subarea, Section, Corridor % of average travel time required for on-time arrival of given trip, similar to Planning Time Index 95th Percentile Duration, Variability Section or Corridor Trip duration in minutes and seconds Exhibit 8.3. Recommended measures for reporting travel time, delay, and reliability. applications involves one or more fundamental tasks, such as identifying the most suitable measures, data collection, forecasting performance under a hypothetical or future con- dition, reporting the results, etc. These building blocks are each addressed in Chapters 2 through 7, and several of these building blocks might be used in each of the above planning applications. For example, the fourth example application is prediction of future conditions. This is typically conducted in order to identify corridors, facilities, or specific locations that at some future point will fail to meet an agency’s standards or require additional investment to serve growing demand in developing areas. The analyst also typically will use the existing and pro- jected future performance data to identify probable cause of the failures, as well as to suggest potential solutions to be eval- uated in a subsequent alternatives analysis. The description of this particular planning application identifies five distinct steps to be taken: Step 1. Determine agency performance standards, Step 2. Determine scope of analysis, Step 3. Select forecast approach,

61 Step 4. Conduct forecasts, and Step 5. Process results. Each of these steps then is covered in detail in a particular chapter (e.g., Chapters 2 and 5 contain guidance for identify- ing and quantifying agency standards, and Chapter 6 de- scribes various methods for estimating or forecasting future values of travel time depending upon the data available). This modular approach is offered because the six planning applications share many common steps (e.g., identification of desired measures, data collection, and forecasting future values of input variables to the performance measures). Presenting these steps or building blocks in discrete chapters eliminates the need to repeat the steps for multiple planning applications. This format also allows the planner or analyst to assemble various steps, as appropriate, to conduct a plan- ning application other than the six defined in this guidebook. The six applications described here will cover a large percentage of applications that might be found in a trans- portation planning context, and with slight modification can be extended to cover most all situations. 8.6.1 Application 1: Evaluate Trends in Travel Time, Delay, and Reliability The objective of this application is to identify and track overall trends in travel time, delay, and reliability for the pur- poses of preparing a report on agency performance. Many agencies regularly do this, and the typical reporting agency might be a MPO, congestion management agency, state DOT, or transit operator, but also could be city or county transportation units, freight operators, or a national DOT. The report may be prepared monthly, quarterly, or annually. In some cases, the reports are directed at high-level decision makers and stakeholders; and in other cases, may be intended for a broader audience of lay system users or taxpayers. The following is an overview of the recommended proce- dure. References are given to the appropriate chapters for the necessary technical guidance. Within those chapters, addi- tional references are given, where appropriate, to more specific technical background on a particular subject. Step 1. Identify Desired Metrics • Select metrics for travel time, delay, and/or reliability, depending upon issues, audience, and availability of real- time data. (Chapter 2) Step 2. Determine Study Bounds • Decide if O-D times, facility times, or segment times desired; and • Decide on length of analysis periods and time slices within analysis period. (Chapter 2) Step 3. Determine Sampling Plan • Determine if suitable data already exist or if sampling is required; • Decide on number of days, hours, seasons of year for which data desired; and • Determine which hours, days, and weeks to sample. (Chapter 3) Step 4. Prepare Data Collection Plan • Determine required accuracy (confidence interval) of results. • Estimate minimum samples required. • Identify segments, facilities to be sampled. • Determine if the available data covers the necessary geo- graphic areas, facilities, time periods, days, seasons of the years needed for the analysis. • If necessary data not available, select one of the following to supplement or fill gaps in available data: – Step 4A. Data collection technology (loop detectors, GPS/AVI vehicles); or – Step 4B. Estimation methodology (sketch planning, HCM, or BPR curve). • Estimate data collection (and/or estimation) costs and per- sonnel required. • Revisit study bounds and accuracy requirements, and tech- nology if resources insufficient. (Chapter 3) Step 5. Conduct Baseline Data Collection • For field data collection methods see appropriate data col- lection guide (e.g., Travel Time Data Collection Handbook, FHWA Traffic Monitoring Guide, etc.); and • Simultaneously collect weather and incident logs for times and locations of data collection (to be used later to address outlier data). (Chapter 3) Step 6. Process Baseline Results • Set reasonableness bounds for data and eliminate outliers; • Set travel time standard (free-flow, speed limit, or other) against which additional travel time is considered delay; • Compute mean and variance for travel time and delay; • Compute confidence intervals for mean travel time and mean delay;

62 Index Value 1.9 1.8 1.7 1.6 1.5 1.4 1.3 1.2 1.1 1.0 2000 2001 2002 2003 1.39 1.66 1.35 1.75 1.42 1.85 1.29 1.54 192 miles 412 miles 412 miles 411 miles Year Exhibit 8.4. Use of congestion and reliability measures to exhibit two differing trends. • Compute desired metrics, selecting from initial list; and • Prepare report and graphics. (Chapter 3) Step 7. Conduct Trend Data Collection • See Step 5, Conduct Baseline Data Collection for guidance. (Chapter 3) Step 8. Process Trend Results • See Step 6, Process Baseline Results for guidance. (Chapter 3) Step 9. Compare Trend to Baseline • Determine extent to which differences between base and trend year are due to sampling error; • Fit trend line to data; and • Prepare report and graphics. (Chapters 3 and 8) Exhibit 8.4 shows how results from two related measures can be compared to one another to help tell a more complete story of trends. This figure illustrates four-year trends in the travel-time index and the planning-time index at the system level. The TTI (represented by the shorter, lighter-colored bars) shows that the typical (i.e., average observed) peak- period trip takes about 30 percent to 40 percent longer than the same trip at FFS, and that trends may be improving in the most recent year presented. The planning time index (PTI, taller, darker-colored bars) represents the additional propor- tion of time travelers should add to a typical free-flow travel time when a 95 percent likelihood of on-time arrival is de- sired. As defined in Chapter 2, the PTI differs from the TTI, as it is based on the 95th percentile trip time or rate, rather than the average rate. The PTI compares near-worst case travel time to light or free-flow travel time, whereas the TTI compares average (measured or estimated) travel time (or rate) to free-flow conditions. In this example, reporting both the TTI and PTI in a com- parative graph may help in interpreting the underlying causes of change in the measures. The trend data suggest that the ob- served reduction in TTI in the final year of data (2003) may be due in large part to a decrease in the longest trip times, as indicated by the even sharper drop in the PTI. The PTI will be more sensitive to the 95th percentile trip time (or rate) value, indicating the longest trip times have declined meas- urably since the previous year of data. This type of result may have been the effect of an improved systemwide incident management program, or other system-level improvement that had a more significant impact in reducing the amount of nonrecurring or incident-generated delay. This reduces the spread between the average trip time and the slowest trips on the system. The PTI uses a different standard of performance than the TTI and indicates that travelers need to allow a larger margin than would be suggested by the TTI; it indicates the amount of time that must be planned for important trips. Ex- hibit 8.4 also includes the miles of freeway included each year in the system-level analysis, and while this information is not essential, it provides the user with a yardstick to confirm that

63 system miles have not changed notably in the final three years of data, and thus average trip times (as indicated by the TTI) have not changed simply as a result of expanded system miles. In a planning application such as identifying the likely fu- ture impact of a proposed solution, travel demand model outputs of travel time and congestion will typically not include the component of nonrecurring delay. In these cases, the TRI (see Section 2.4) is used, which does not include in- cident-generated delay. The TRI is also the appropriate meas- ure when travel-time runs are conducted to estimate travel rates, since those runs affected by incident conditions are normally removed from the data set. The TTI and PTI are most appropriate where continuous data streams allow for direct measurement that includes incidents. 8.6.2 Application 2: Identify Existing Deficiencies The objective of this application is to identify and diagnose existing deficiencies in travel time, delay, and reliability for the purposes of determining appropriate agency actions. The outcome of the analysis is usually a report identifying facilities and locations failing to meet the agencies’ perform- ance standards, and identifying the probable causes of the failures. The report may even go on to recommend specific improvements. However, the development of these recom- mendations will be covered under the alternatives analysis application, which is described later. The typical agency may be a transit operator, freight operator, city, county, MPO, congestion management agency, state DOT, or a national DOT. The analysis may be performed when the agency first becomes aware of a problem or may be done annually, or linked to some other regular pe- riod (e.g., a budget cycle, a long-range plan update, etc.). Step 1. Determine Agency Performance Standards • Select metrics for travel time, delay, and reliability; • Decide if agency performance will be measured in terms of O-D times, facility times, or segment time delay and/or re- liability; and • Determine agency performance standards for each metric. (Chapters 2 and 5) Step 2. Determine Sampling Plan for Determining Compliance • Decide on number of days, hours, seasons of year for which data desired; and • Determine which hours, days, and weeks to sample. (Chapter 3) Step 3. Prepare Data Collection Plan • Determine whether real-time detector data exists; • Determine required accuracy (confidence interval) of results; • Estimate minimum samples required; • Identify facilities and segments to be included; • Determine if the available detector data covers the neces- sary geographic areas, facilities, time periods, days, seasons of the years needed for the analysis; • If necessary detector data is not available, select data collection technology (loop detectors, GPS/AVI vehicles) or estimation methodology (sketch planning, HCM, or BPR curve) to supplement or fill gaps in available detector data; • Estimate data collection costs and personnel required; and • Revisit study bounds and accuracy requirements, and tech- nology if resources insufficient. (Chapters 3 and 5) Step 4. Conduct Data Collection • For field data collection methods see Introduction to Traf- fic Engineering - A Manual for Data Collection (8) or ITE Manual of Traffic Engineering Studies (9); and • Simultaneously collect weather and incident logs for times and locations of data collection (to be used later for diag- nosis). (Chapters 3 and 5) Step 5. Process Results • Set reasonableness bounds for data and eliminate outliers; • Set travel time standard (free-flow, speed limit, or other) against which additional travel time is considered delay; • Compute mean and variance for travel time and delay; • Compute confidence intervals for mean travel time and mean delay; • Compute desired reliability metrics; and • Identify deficient segments and facilities (Chapter 5) Step 6. Diagnose Causes of Deficiencies • Cross-tabulate incident log against measured performance deficiencies; • Note geometric constraints; • Identify volume increase locations; • Identify cause of deficiency; and • Prepare report. (Chapters 5 and 8) Exhibit 8.5 presents an example of trend data plotted against agency performance standards. In this case the measure is the

64 Percent of Lane Miles Percent of Arterial Lane Miles with Volumes < 10,000 Vehicles per lane, per day (78% Short-Term Target; 73% Long-Term Target) Percent of Freeway Lane Miles with Volumes < 20,000 Vehicles per lane, per day (66% Short-Term Target; 61% Long-Term Target) 92.2% 90.7% 88.4% 88.2% 88.1% 85.2% 83.4% 83.1% 83.4% 81.3% 80.6% 81.1% 81.7% 78.1% 74.1% 70.6% 71.0% 70.4% 50 60 70 80 90 100 1996 1997 1998 1999 2000 2001 2002 2003 2004 Calendar Year 61% Long-Term Target 73% Long-Term Target Exhibit 8.5. Comparison of trend to agency performance standard. Percent- age of lane miles with average annual volumes below congested levels. percentage of lane miles that are operating at uncongested lev- els. The agency performance standard is set as a minimum (i.e., they want to see no less than 73 percent of their lower-volume roads, and no less than 61 percent of their higher-volume roads), operating at uncongested levels. The trend data indi- cate that although both lower- and higher-volume roadways still exceed the agency performance standard, there has been a steady downward trend (i.e., negative) over the years data is presented. Depending upon the underlying causes for the grad- ual degradation in performance (e.g., rising VMT and density per highway lane mile), the data suggest that more aggressive countermeasures, possibly both capital and operating, will be needed to maintain above-target performance over the long term. 8.6.3 Application 3: Evaluation of Effectiveness of Improvements The objective of this application is to determine if an im- plemented improvement or action actually resulted in the de- sired improvement in travel time, delay, or reliability. This type of analysis allows an agency to better assess the cost ef- fectiveness of specific actions and also to assess the effective- ness of their planning analysis and decision processes. Any typical agency with responsibility and accountability for ex- penditure of funds for system improvements and operations may at times need to conduct a careful before/after analysis such as this. The report would be prepared one time only for each improvement evaluated, rather than on an ongoing or periodic basis. Chapter 4 contains specific guidance on the before/after type of application. Step 1. Identify Desired Metrics • Select metrics for travel time, delay, and reliability. In this particular application where comparison of before and after performance is required, special attention must be given to measure selection to ensure that data and measures from the two time periods are in fact comparable. This constraint may limit the range of measures available for the comparison, particularly if the decision to conduct the before/after analy- sis was not made until after implementation of the improve- ment, in which case, the analyst is limited to data on-hand representative of the before-project conditions. It is always preferable, though not always possible, to develop the be- fore/after analysis framework and data collection plan before any construction on the improvement has taken place. (Chapters 2 and 4) Step 2. Determine Study Bounds • Decide if O-D times, facility times, or segment times de- sired; and • Decide on length of analysis periods and time slices within analysis period. (Chapter 2)

65 Step 3. Determine Sampling Plan • Decide on number of days, hours, seasons of year for which data desired; and • Determine which hours, days, and weeks to sample. (Chapter 3) Step 4. Prepare Data Collection Plan • Determine desired lag time between implementation of the facility or system improvement and the measurement of its success or failure; • Identify segments and facilities to be sampled; • Determine if the available detector data covers the necessary geographic areas, facilities, time periods, days, and seasons needed for the analysis; • Determine required accuracy (confidence interval) of results; • Estimate minimum samples required; • If necessary detector data not available, select data collection technology (loop detectors, GPS/AVI vehicles) or estima- tion methodology (sketch planning, HCM, or BPR curve) to supplement or fill gaps in available detector data; • Estimate data collection (and/or estimation) costs and personnel required; and • Revisit study bounds and accuracy requirements, and technology if resources insufficient. (Chapter 3) Step 5. Conduct Baseline (Before) Data Collection • For field data collection methods, see ITE Data Collection Guide; and • Simultaneously collect weather and incident logs for times and locations of data collection (to be used later to address outlier data). (Chapter 3) Step 6. Process Baseline Results • Set reasonableness bounds for data and eliminate outliers; • Set travel time standard (free-flow, speed limit, or other) against which additional travel time is considered delay; • Compute mean and variance for travel time and delay; • Compute confidence intervals for mean travel time and mean delay; • Compute desired reliability metrics; and • Prepare report and graphics. (Chapter 3) Step 7. Conduct “After” Data Collection • See Step 5, Conduct Baseline Data Collection for Guidance. (Chapter 3) Step 8. Process After Results • See Step 6, Process Baseline Results for guidance. (Chapter 3) Step 9. Compare Before and After Results • Determine extent to which differences between base and trend year are due to sampling error; • Conduct hypothesis tests of before/after results improve- ments to determine statistical significance of results; • Prepare report and graphics; and • Revise monitoring plan for future analyses. (Chapter 4) 8.6.4 Application 4: Prediction of Future Conditions The typical objective of this application is to identify and diagnose future deficiencies in travel time, delay, and/or reli- ability for the purposes of determining appropriate agency actions. The outcome of the analysis is usually a report iden- tifying facilities and locations failing to meet the agencies’ standards at some future date, and identifying the probable causes of the failures. The performance report may go on to recommend specific improvements to address deficiencies. However, the devel- opment of these recommendations will be covered later under the alternatives analysis application. Step 1. Determine Agency Performance Standards • Select metrics for travel time, delay, and reliability; • Decide if agency performance will be measured in terms of O-D times, facility times, or segment times delay and/or re- liability; and • Determine agency performance standards for each metric. (Chapter 2) Step 2. Determine Scope of Analysis • Determine temporal scope of analysis; • Decide on number of days, hours, seasons of year for which results desired; • Determine which existing and forecast years, hours, days, and weeks to evaluate; • Determine geographic scope of analysis; • Determine which trip O-Ds, which facilities, and/or which segments of facilities to evaluate; and • Determine required outputs of analysis and accuracy (con- fidence interval) of results. (Chapters 2 and 3)

66 Step 3. Select Forecast Approach • Determine resources (funds, time, personnel) available for analysis; • Select desired analytical approach (e.g., sketch planning, 4-step, mezoscopic, HCM, micro-simulation); and • Revisit accuracy requirements, proposed analytical ap- proach, and number of candidate improvements if inade- quate resources or time. (Chapter 6) Step 4. Conduct Forecasts • For 4-step model approach, see the FHWA Guide on Travel Forecasting; • For microsimulation, see the FHWA Guide on Micro- simulation; • For HCM analysis, see HCM; and • For sketch planning, see NCHRP 398: Congestion Mea- surement. (Chapter 6) Step 5. Process Results • Set reasonableness bounds for forecasts and eliminate outliers; • Set travel time standard (free-flow, speed limit, or other) against which additional travel time is considered delay; • Compute mean and variance for travel time and delay; • Compute confidence intervals for mean travel time and mean delay; • Compute desired reliability metrics; • Identify deficiencies; and • Prepare report and graphics. (Chapter 3) 8.6.5 Application 5: Alternatives Analysis The objective of this application is to develop and evaluate a set of alternative actions to improve facility or system performance. Presumably, the operator already has con- ducted Application 2: Identification of Existing Deficiencies, and has diagnosed the underlying causes of the existing prob- lems. The operator also should have conducted a future analysis (Application 4) and identified future deficiencies and their projected causes. The outcome of the alternatives analysis is usually a report identifying facilities that currently fail and/or in the future will fail to meet the agency’s standards, reviewing the probable causes of the failures, and recommending actions by the agency (and potentially other agencies) to alleviate the existing and/or future deficiencies. The typical agency may be a transit operator, freight operator, city, county, MPO, congestion management agency, state DOT, or a national DOT. The analysis may be performed when the agency first becomes aware of a prob- lem, usually as the outcome of a periodic monitoring of sys- tem performance, such as might be produced by Application 2: Identification of Deficiencies. Many agencies also conduct regional system or corridor analyses to identify projected future deficiencies and test the efficacy of different capital and operating strategies. Step 1. Conduct Studies to Identify and Diagnose Existing and Future Deficiencies • These studies should be completed prior to conducting the alternatives analysis: Application 2: Identification of Exist- ing Deficiencies and Application 4: Predictions of Future Conditions. Step 2. Determine Candidate Improvements • The analyst should consult a number of sources to identify potential solutions that address the identified deficiencies. Chapter 7 presents in table format a collection of typical problems, likely causes, and improvement strategies and actions. It also references several published reference doc- uments that can guide the analyst to strategies and actions that are specifically appropriate for reducing travel time, delay, and variability. (Chapter 7) Step 3. Determine Scope of Analysis • Determine temporal scope of analysis; • Decide on number of days, hours, seasons of year for which results desired; • Determine which existing and forecast years, which hours, which days, which weeks to evaluate; • Determine geographic scope of analysis; • Determine which trip O-D’s, which facilities and/or which segments of facilities; and • Determine required outputs of analysis and accuracy (con- fidence interval) of results. (Chapters 2 and 3) Step 4. Select Evaluation Approach • Determine resources (funds, time, personnel) available for analysis; • Select desired analytical approach (e.g., sketch planning, 4-step, mezoscopic, HCM, micro-simulation); and

67 • Revisit accuracy requirements, proposed analytical ap- proach, and number of candidate improvements if inade- quate resources or time. (Chapter 3) Step 5. Evaluate Improvements • Estimate mean travel time, delay, reliability before and after improvement (The methodology provided here will vary according to the selected approach in the prior step.); • Compute reliability metrics as desired; • Determine confidence intervals for results; • Estimate cost-effectiveness of each candidate improve- ment; • Determine if candidate improvements are sufficient to meet operator standards; and • Select final list of improvements. (Chapter 3) Step 6. Develop Improvement Program • Determine funds available for improvements; • Determine desired timeline and sequence for improve- ments; • Prioritize and schedule improvements; • Determine needed funding schedule; • Prepare report and graphics; and • Revise monitoring plan for future analyses. (Material not explicitly presented in this Guidebook.) 8.6.6 Application 6: Improve Fleet Operations and Productivity The objective of this application is to develop a set of actions to improve fleet operations and productivity. Presumably, the operator already has conducted Application 2: Identification of Existing Deficiencies, and has diagnosed the existing causes of the problems. The operator may have arrived at this point after conducting a future analysis (Application 4) and identi- fying future deficiencies. The outcome of the analysis for Fleet Operations and Productivity is usually a report identifying vehicle routes that currently fail and/or in the future will fail to meet the operator’s standards, reviewing the probable causes of the failures, and rec- ommending actions by the operator (and potentially other agencies) to alleviate the existing and/or future deficiencies. The typical fleet operator may be a transit operator or a freight operator, or a planning agency with responsibility for oversight of transit performance. The analysis may be performed when the agency first becomes aware of a problem, either through customer feedback or perhaps as the outcome of periodic monitoring of system performance, such as might be produced by Application 2: Identify Existing Deficiencies. Step 1. Conduct Studies to Identify and Diagnose Deficiencies • These studies should be completed prior to conducting the alternatives analysis: Application 2: Identification of Existing Deficiencies and Application 4: Predictions of Future Conditions. Step 2. Determine Candidate Improvements • Exhibit 7.1 in Chapter 7 may be used to identify appropriate candidate improvements to consider for solving the identi- fied deficiencies, particularly if the deficiencies are related to roadway system capacity, and are impacting movement of trucks or transit vehicles on the general purpose highway network. For deficiencies specific to the fleet operation itself (e.g., maintenance, route designation and run scheduling), more specialized resource materials, outside of the scope of this effort, should be consulted. Suggested references include TCRP Report 95: Traveler Response to Transportation System Changes, and TCRP Report 100: Transit Capacity and Level of Service Manual. These reports are available on-line at TRB.org/TRB/publications. Step 3. Determine Scope of Analysis • Determine temporal scope of analysis; • Decide on number of days, hours, seasons of year for which results desired; • Determine which existing and forecast years, which hours, which days, which weeks to evaluate; • Determine geographic scope of analysis; • Determine which trip O-D’s, which facilities and/or which segments of facilities; and • Determine required outputs of analysis and accuracy (con- fidence interval) of results. (Chapters 2 and 3) Step 4. Select Evaluation Approach • Determine resources (funds, time, personnel) available for analysis; • Select desired analytical approach (e.g., sketch planning, 4-step, mezoscopic, HCM, micro-simulation); and • Revisit accuracy requirements, proposed analytical ap- proach, and number of candidate improvements if inade- quate resources or time. (Chapter 3)

68 Step 5. Evaluate Improvements • Estimate mean travel time, delay, reliability before and after improvement (the methodology provided here will vary according to the selected approach in the prior step); • Compute reliability metrics as desired; • Determine confidence intervals for results; • Estimate cost-effectiveness of each candidate improve- ment; • Determine if candidate improvements are sufficient to meet operator standards; and • Select final list of improvements. (Chapters 3 and 4) Step 6. Develop Improvement Program • Determine funds available for improvements; • Determine desired timeline and sequence for improvements; • Prioritize and schedule improvements; • Determine needed funding schedule; • Prepare report; and • Revise monitoring plan for future analyses. (Material not explicitly presented in this guidebook.)

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TRB's National Cooperative Highway Research Program (NCHRP) Report 618: Cost-Effective Performance Measures for Travel Time Delay, Variation, and Reliability explores a framework and methods to predict, measure, and report travel time, delay, and reliability from a customer-oriented perspective.

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