CHAPTER 1

INTRODUCTION

This guidebook is intended to serve as a resource for transportation agency personnel (hereafter referred to as the “reader”) to identify the needs they may have for a travel time monitoring system and then develop a functional specification for a system that meets those needs.

OVERVIEW

Transportation involves the movement of people and goods in space, time, and condition. While the time involved in travel has always been important, in recent years travel time reliability has become significantly more critical, especially for highway networks. Congestion and incidents have begun to have impacts on the reliability of travel time that cannot be overlooked. The risks of being late, or early, are real, and either one involves costs: of missing important events or failing to make good use of the time available.

Road networks today are struggling to provide the quality of service for which they were originally designed and built. Rising demand has caused increased congestion, both recurring and nonrecurring. And as congestion has increased, travel times have become longer and more variant due to recurrent events of demand exceeding capacity during peak periods of travel and non-recurrent events such as incidents, weather, and special events. Given the difficulty in adding enough base capacity to reduce the impact of these variations, transportation agencies have increasingly begun to turn to transportation system management to minimize travel time variability and provide route choice and departure time guidance to travelers and freight shippers.

Reliability is one of the four focus areas of the Strategic Highway Research Program (SHRP) 2, authorized by Congress in 2006. The purpose of the reliability focus area is to “reduce congestion and improve travel time reliability through incident management, response, and mitigation” (1. Four themes have been established under this focus area:

Theme 1: Data, Metrics, Analysis, and Decision Support Theme 2: Institutional Change, Human Behavior, and Resource Needs Theme 3: Incorporating Reliability into Planning, Programming, and Design Theme 4: Fostering Innovation to Improve Travel Time Reliability

The project under which this guidebook has been prepared is SHRP2 Project L02, Establishing Monitoring Programs for Travel Time Reliability. The project supports the first of these themes. It aims to create methods by which travel time reliability can be monitored, assessed, and communicated to end users of the transportation system. The project seeks to provide guidance to operating agencies about how they can put better measurement methods into practice by enhancing existing monitoring systems or creating new ones. It aims to validate those methods through field studies that take a fresh look at travel time reliability, its measurement, and its relationship to the seven major influencing factors that have been identified:

Traffic incidents, Work zones,

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Weather, Special events, Traffic control devices, Fluctuations in demand, and Inadequate base capacity.

This project is part of a substantial investment that has been made to create a system-based approach to investment decision making, including performance monitoring. The National Cooperative Highway Research Program (NCHRP) has explored the effectiveness of various operational improvements in a number of recent projects. These include:

Valuation of Travel-Time Savings and Predictability in Congested Conditions for Highway User-Cost Estimation (NCHRP 02-18(2)),

Understanding the Contribution of Operations, Technology, and Design to Meeting Highway Capacity Needs (SHRP2 Project C05),

Strategies for integrated operation of freeway/ arterial corridors (NCHRP Project 3-81),

Maximizing freeway throughput under threat of breakdown flow (NCHRP Project 3-87),

The effectiveness of low-cost improvements in the vicinity of freeway bottlenecks (NCHRP Project 3-83),

An analysis of freeway weaving sections (NCHRP Project 3-75), A comprehensive assessment of roundabout applications in the United States

(NCHRP Project 3-65), and An evaluation of the effects of Right Turn Lanes (NCHRP Project 3-72).

Two distinct Federal Highway Administration programs offer the potential to provide significant input into this project as well: the Integrated Corridor Management (ICM) program and the Vehicle Infrastructure Initiative (VII). The ICM effort seeks to take advantage of unused capacity in transportation corridors by managing assets comprehensively as opposed to the fragmented approach taken today. The VII program promises more specific improvements to harnessing technology for safety and mobility benefits. This project also takes into account the information available in NCHRP Report 431, which provides a treatment of reliability from the economic perspective, i.e., provides valuation of travel-time savings and predictability of travel times under congested conditions.

In this context, the SHRP2 program has become the next major milestone in the evolution of transportation thought and practice. With its products, transportation practitioners will be better prepared to think in terms of a transportation system rather than the individual facilities and modes that make it up. The development of a framework for collaborative decision making (SHRP2 Project C01) is a flagship effort in this regard, and its success depends on input from this particular project as well as several others within the SHRP2 program. Ultimately, a collaborative and multidimensional approach to managing, enhancing, and investing in the highway transportation system will lead transportation professionals to new levels of efficiency and productivity that cannot otherwise be achieved.

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WHAT IS TRAVEL TIME RELIABILITY?

A number of definitions are helpful in clarifying the meaning of travel time reliability and its measurement. While no consensus yet exists, Elefteriadou and Ciu (2) provide a good starting basis in their study of TTR definitions. As they discern, Ebeling (3) provides a useful basic idea, defining reliability as “the probability that a component or system will perform a required function for a given period of time when used under stated operating conditions. It is the probability of a non-failure over time.” This is a widely accepted definition that makes itself manifest in mean-times-between-failure (MTBF) for devices, repeatability expectations for measurement systems, validity measures for testing protocols, and quality control tools for manufacturing processes. It is slightly different from the idea of consistency, which has to do with the absence of variance.

Brought into the world of transportation, this reliability idea means a traveler or a shipper expects to experience an actual time of arrival (ATA) which matches a desired time of arrival (DTA) within some window for a given trip or shipment, as shown in Exhibit 1-1. In some cases the ATA and DTA can be extremely important; in other cases they are not, depending on how constrained the trip is. For example, a trip to the airport to catch a plane would be more constrained than a trip to the store to buy groceries because it is more important that the ATA is within a closer range of the DTA for the trip to the airport than to the store. The consequences of an ATA outside the DTA, especially an ATA after the DTA, are more severe for the trip to the airport because an ATA outside the DTA could result in missing a flight.

The transportation system is successful in providing the desired travel if and only if the ATA “matches” the DTA and it “fails” if it does not. As Elefteriadou and Ciu point out, the definition of reliability becomes well defined if an unambiguous and observable description of success is provided, including the unit of time over which success will be evaluated. In other words, success occurs if the ATA is inside the DTA window; the “unit of time over which failure will be evaluated”; and otherwise, it “fails.”

In addition, borrowing the ideas in decision theory, as described for example by Hannson (4), utility (usefulness) is maximized if the ATA is inside the DTA window. Conversely, disutility is greater if the ATA lies outside the DTA window; and the aggregate disutility for all trips among all users is the “societal cost” of the reliability experienced. The function that evaluates the disutility may be symmetric or asymmetric depending on the situation, as shown in Exhibit 1-2. Truckers incur significant penalties if they are either late or early in delivering shipments to the receivers. People can be late for appointments or miss the opportunity to insert additional tasks like stopping for coffee or sleeping later if they are early.

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Exhibit 1-1ATA and DTA Concepts

Exhibit 1-2Disutility Function If the trips are all observable, with the DTA windows being known, then the reliability of

a given transportation system can actually be assessed. One can compute the percent of ATAs that fall within their DTA windows. This is a useful metric both for the entities making the trips as well as the organizations providing the service (e.g., the TMC or transit operator). The aggregate disutility can also be computed by summing the disutility values for each trip.

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Noland et al. (5) did some early exploration of this issue finding that travel time consistency did have a bearing on route choice. It was found that the perceived disutility u of a given trip could be related to the travel time mean μt and standard deviation σt as shown in Equation 1-1.

Equation 1-1

u=−0.0996 μt−0.1263σt

The model was calibrated through a stated preference survey involving 700 commuters in the Los Angeles region with 4,340 observations. The ratio of the coefficients (0.1263 / 0.0996) suggests that the respondents think the travel time standard deviation σt is 27 percent more than the mean μt. Put another way, each minute of standard deviation is 27 percent more costly than each minute of mean travel time. To a certain extent, the respondents would rather take a route with longer, more consistent travel times than a route with shorter average travel times but greater variability.

The problem with trying to compute the overall disutility for travel using surface transportation is that the necessary data are not presently obtainable. In the airline industry, ATAs and surrogate DTA windows are known since flights operate on a fixed schedule and ATAs are collected and recorded. However, surface transportation DTAs are hard to identify on a large scale (beyond a specific individuals’ DTA) and then match to specific ATAs. This metric is also more focused on the demand side of transportation (the customer) than the supply side (the service provider). There is a need for metrics and measures that can be used today, given the observability of the system, that help users and service providers make intelligent, informed decisions.

What can be observed today, at least in part, are the travel times. The most advanced traffic management centers (TMCs) can monitor probes, vehicles equipped with tags in areas where toll roads exist, and others can generate speed distributions at specific point locations in the network where sensors (speed traps) are installed.

As a result, a service provider (e.g., the TMC or the transit authority), can establish a vector of target travel times to be achieved for trips between specific origins and destinations, consistent with Ebeling (3). De facto, this is a set of desired travel times (DTTs) or DTT windows for the system. These DTTs can be dependent upon the departure time to account for congestion effects, and they can be adjusted over time as network loading grows and/or changes (and capacity investments are made or operating plans are altered). However, they should not be mean values or percentiles, since those are traffic-dependent and vary with time and conditions.

Success arises for a trip when its actual travel time (ATT) falls within the allowable DTT window based on the departure time; otherwise, the system has failed. Reliability is measured by the percentage of trips whose ATTs fall within the DTT windows. By extension, the aggregate disutility experienced by the travelers or shippers can be assessed, in principle, using disutility functions which compare the ATTs one-at-a-time with their corresponding DTTs and then sums the results.

In addition to providing vectors of target travel times, service providers can establish pace targets (travel times per unit distance) to be achieved during different conditions. Success for a trip or subtrip can then occur if the actual trip rate (ATR) falls within the acceptable DTR window. A problem with this is that individual point-to-point ATRs may all be deemed successes while the trip counts as a failure because the ATRs happen to be consistently low or

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high relative to the DTR windows (e.g., due to driver behavior), which puts the ATT outside the allowable DTT window. A second problem is that it imposes pace targets universally across the entire network regardless of variations in demand-to-supply ratios (congestion) or facility attributes.

Ultimately, the travel time reliability needs of an individual depend on how they will use the reliability information. Service customers (travelers and shippers) need to know when to start a trip (or commence a shipment) and what route to employ (or expedited service to select) so that their ATA will be within their DTA window; and service providers need to know where to make capacity investments and how to change the operating plan so that network ATTs match DTTs. Both a focus on reliability and an emphasis on consistency help in this regard.

The temporal context of the decision affected by travel time reliability information also matters. Ex post facto, service customers assess their departure time and route choice decisions based on historical data. They need to know if their ATAs are matching their DTAs or if a change in tactic is needed: earlier departure times, alternate routes, alternative modes, or a willingness to pay higher prices. Pre-trip, they need to have the best available data (historic blended with real-time and predictions) to make choices most likely to produce ATAs within their DTA windows. En route, they want to shift routes (based on input from their route guidance device, either on the dash or in their head) to keep estimated times of arrival (ETA) as close to the DTAs as possible.

Service providers want to see if different ways to operate the system would be likely to produce better alignment between the ATTs and the DTTs (or if capacity investments are needed). Naturally, this decision making is aimed at variance reduction and shifts in the mean values either lower or higher so that the requisite confidence interval objectives are met given the DTT windows.

The decision making is very akin to the mean-variance tradeoff analyses so prevalent in financial planning (see, for example, Maginn, Tuttle, McLeavey, and Pinto, (6)) where risk (reflected in the variance) is traded off against reward (reflected in the mean). In real-time, transportation service providers want to know if they are making the right tactical and operational decisions (as in incident management or the assignment of capacity through toll booths, reversible lanes, signal timing, etc.) to best manage the tradeoff between mean travel times and travel time variance, to keep the ATTs aligned with the DTT windows.

USE CASES

A functioning reliability monitoring system must meet the needs of many different types of users because different users perceive and value deviations from the expected travel time in different ways. Each of these user classes has different motivations for monitoring travel time reliability, and these needs have to be accounted for in the types of analysis that the system can support through the user interface. Use cases are a formal systems engineering construct that transforms user needs into short, descriptive narratives that describe a system’s behavior. Use cases are used to capture a system's behavioral requirements by detailing scenario-driven threads through the functional requirements. The collective use cases define the monitoring system by capturing its functionalities and applications for various users.

Appendix X provides a series of use cases to help readers of the guidebook determine what information the travel time reliability monitoring system needs to produce and what applications it needs to have to satisfy their specific situation. Once the appropriate users and their needs for reliability information are defined, the guidebook reader can determine the

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performance measures, spatial coverage, data interface needs (i.e., weather, crashes, construction activity, special events), and archival requirements for their monitoring system. The appendix also highlights the similarities in data needs among the use cases to show additional ways to use the specific data that a reliability monitoring system is already collecting.

The use cases are organized around the various stakeholders that use or manage aspects of the highway-based transportation system:

Users and managers of the roadway system, which includes the network of freeways, arterials, collectors, and local streets that serve private autos, trucks, and transit vehicles.

Users and managers of the transit system that operates on the highway network. This primarily refers to buses and light rail vehicles in this guidebook.

Users and operators of the freight system, which in this guidebook primarily refers to shippers, receivers, and truck-based carriers.

Decision-makers who determine policy or are responsible for planning and performance measurement, which includes high-level decision-makers that affect the day-to-day operations and long-term performance of the transportation system.

In addition to classifying the users of the reliability monitoring system by transportation system component, the users can also be viewed from a two-tier perspective: service providers and service consumers. Service providers include road operators (private or public), transit operators, shipping companies, and policy makers. Service customers (also thought of as service consumers) include commuters, truck drivers, transit drivers, transit passengers, and freight customers.

DEMAND-SIDE AND SUPPLY-SIDE PERSPECTIVES

Travel time reliability can be viewed from the unique demand-side and supply-side perspectives, both of which are addressed in this guidebook.

The demand-side perspective focuses on individual travelers and the travel times they might experience in making trips. Observations of individual vehicle (trip) travel times are what are important, and the travel time density functions of interest pertain to travel times experienced by individual travelers at specific points in time. That means the reported percentiles of those travel times (e.g., the 15th, 50th, and 85th percentiles) pertain to the travel times for those travelers.

The graph in Exhibit 1-3 shows the demand-side perspective of travel times. The graph shows the travel times for eight travelers over 20 days for a common trip type and time period. Each user experiences variability in travel time among the days. On a specific day, there is also variability among the eight users.

Exhibit 1-3Illustration of Demand-Side Perspective The supply side perspective focuses on consistency in the travel times for segments or

areas. The observations are average (or median) travel times on these segments or routes in specific time periods (e.g., 5 minute intervals); the average is the mean of these 5-minute average travel time observations; and the probability density functions capture the variation in these average travel times in some time period. The percentile observations pertain to specific points in the probability density function for these observed average travel time values. The word

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“average” is used wherever average travel times are being referenced just to ensure that the text is clear and the reader does not mistakenly perceive that actual trip travel times are being discussed.

The graph in Exhibit 1-4 shows the minimum, average, and maximum travel times for the eight users depicted in Exhibit 1-3. No one specific user experiences the exact travel times represented by the minimum, nor does any one user experience the average travel times or the maximum travel times. A single user may experience the minimum travel time on one day and the maximum travel time the next day. The graph shows characteristics of travel times on the segment as a whole.

Exhibit 1-4Illustration of Supply-Side Perspective Both of these perspectives are important. The first relates to the demand side of the travel

market place, where individual travelers want to receive information that helps them ascertain when they need to leave to arrive on time or how long a given trip is likely to take given some degree of confidence in the arrival time. The second aims at the supply side for system operators interested in understanding the consistency in segment- and route-level travel times provided and in making capital investments and operational changes that help enhance that consistency.

TRIP CONCEPTS

Several concepts at the trip level are important in understanding the methodology presented in this guidebook. Some of the ideas are already in common use because people have been studying travel time reliability for several years. Others are new, reflecting the insights obtained by the authors of this guidebook.

Persons and Packages

The first and most important tenet is that persons and packages are the fundamental entities about which travel time reliability analyses are focused. This means the trips have to do with people and packages. Personal travel time reliability has to do with individual person trips, and freight travel time reliability has to do with individual package shipments. The reason is that it is individual people who are early or late for appointments or flight departures, and it is individual packages that are delivered early, late or on time.

This having been said, aggregate measures of those trips are also meaningful and insightful. Moreover, such data are also easier to collect. For highways, individual person travel times are very difficult to observe. Even observing individual vehicle travel times is challenging. However as vehicles equipped with automatic vehicle identification (AVI) and automatic vehicle location (AVL) become more prevalent, it will become easier to collect individual vehicle travel times. Also, single occupant vehicles are a significant portion of the traffic stream, especially during congested conditions, so insights about individual person trips can be seen in the vehicle data. For freight trips, similar statements pertain. Packages are bundled into shipments; and shipments are often bundled into truck loads, so focusing on truck trips does provide insights into shipment and package travel times.

It is important to note the difference between travel time and trip time when analyzing travel time reliability. Trip time refers to the elapsed time between a sensor identifying a vehicle

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and a downstream sensor re-identifying the vehicle. Trip times could include stops and/or detours, since the specific route of the vehicle between the two sensors is unknown. Travel time refers to the actual driving time between the sensors. Trip times need to be filtered in order to obtain accurate ground truth travel times.

These things having been said, the main point is that travel time reliability, and the ideas about it presented in this document, are intended to address questions about individual person and package trips even though surrogate data are used. This means the methodology is designed to provide insights about the travel time reliability that might be experienced by persons and packages by extension of the insights derived from analyses of more aggregate measures.

Probability Density Functions

The next thought is that all metrics of interest can be derived from probability density functions (PDFs). They describe the travel times or travel rates (travel times per unit distance). Hence, the typical metrics of interest for characterizing reliability—planning index, buffer index, average, median, 95th percentile, or others—can be computed based on the PDFs. Hence, these PDFs, supplemented by ancillary data about the environment that does (or will exist) in the timeframe of the analysis (e.g., weather, incidents), represent sufficient information to answer the questions.

Related to this, the PDFs can be multimodal (in a statistical sense, meaning more than one point at which the probability density function reaches a maximum). This is because the data may reflect more than one operating condition or regime across the span of time being studied or the users may have experienced different treatments during the analysis timeframe. An illustration of the former arises when the average travel times for a given 5-minute time slice are studied across a year. It is likely that different operating conditions existed on different days (because of weather and incidents let alone different demand levels), so multimodality should be expected. An illustration of the latter is when travelers do not all experience the same control treatment. For arterial networks, this could arise when some users are able to progress between traffic signals without stopping while others are not. For freeway networks, it can be because some vehicles are delayed by ramp metering controls while others are not; or when some vehicles experience delays from paying tolls (e.g., cash) while others do not (e.g., toll tags).

Since the word “mode” is used in other ways in transportation, the word regime is used instead of mode to describe these various operating conditions (or sub-conditions). Moreover, common traffic engineering names are used to describe these modes like “congested”, “uncongested”, “transition”, “incident”, “weather”, etc. The regimes help enhance the quality of the PDFs. It keeps them from being noisy, and it helps maximize the incremental value derived from the data acquired every day.

As an illustration, Exhibit 1-5 shows histograms of travel times during conditions without an incident, weather, or special event and during conditions with an incident. Exhibit 1-6 shows the corresponding PDFs for the two conditions. The PDF is a density function based on the values in the histograms.

Exhibit 1-5Weekday AM Travel Time Histograms for Various Event Conditions Exhibit 1-6PDFs of Weekday AM Travel Times for Various Event Conditions

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Travel Rate

Another idea is travel rate. This is travel time per unit distance, as in minutes per mile. While travel times help travelers understand how long it will take them to accomplish their trips—and help system managers determine what travel times to display on variable message signs, as an example—travel rates help system managers compare the performance of one segment with another so that strategies can be developed for making capital investments that help to improve the performance of the segments and the network as a whole. Travel rate PDFs are distinguished from travel time PDFs by notation (TR-PDFs versus TT-PDFs).

NETWORK CONCEPTS

The next challenge is to apply these concepts to the physical world. Notions of nodes and links need to be created as well as ideas about how link travel times are developed and then added together to get overall trip travel times and rates.

Monuments

The first idea is monuments. These are reference points (nodes) to and from which the travel times are measured. While monuments can be placed at the ends of the physical links (i.e., in the middle of intersections or interchanges), the findings from this study suggest this is not wise; the travel times become ambiguous because turning movements confound the observations. Placing the monuments at the midpoints of links removes this confusion. It ensures that the correct turning movement delay is included in each monument-to-monument travel time. This is clearly important for arterials but it is also important for freeways. Ramp movements can have different travel times (e.g., direct ramp or loop ramp, as well as and any traffic control on the ramp—such as a signal—as is sometimes the case in Los Angeles).

The monuments should be—and need to be—linked to spots that the traffic management center uses to monitor the system, as in the location of system detectors on both the freeway and arterial networks. This minimizes the database management tasks involved in keeping track of where the monuments are located. The can also be the location of toll tag readers and AVI sensors. They should not be placed at locations where standing queues occur.

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Exhibit 1-7Monuments

Passage Times

Related to the monuments is the idea of passage times. These are the times at which vehicles pass the monuments. When a monument is at the location of a system detector (e.g., loops in the pavement), the passage time is the instant at which the vehicle crosses the detector. In the case of AVI sensors where a toll is collected, it is the time when the toll is collected. Other times would cause confusion. In the case of other AVI sensors, where the tag may be identified more than one time when the vehicle is passing the sensor, it should be the time when the vehicle is closest to the sensor. For Bluetooth sensors, the response with the greatest signal strength should be used, or an interpolation of the response pulses to identify this location, or the mean of the strongest responses. For AVL vehicles, as is the case for some AVL applications, it should be the time when the vehicle passes the monument location based on comparing the latitude and longitude of the vehicle with the latitude and longitude of the monument.

Segments

The next idea is one of segments. These are the paths between the monuments for which the travel times are monitored and estimated. These segments can be—and should be—related to the physical network. One good option is to map them to demarcations of the network used for other purposes (e.g., TMC segments, which are explained later in this chapter). It is the segments for which probability density functions are estimated.

As shown in Exhibit 1-8, an example is a virtual link that ties the midpoint of a north-south link south of an intersection to the midpoint of an east-west link to the east of the

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intersection. The northbound-to-eastbound segment connecting the monuments on these two links would represent the northbound right turn at the intersection. The segment in the other direction would represent the westbound left.

Exhibit 1-8Segments

Routes

The next idea is a route. Segments combine to form routes. A route’s travel time reliability is described by a TT-PDF, either measured directly or assembled based on the TT-PDFs for the segments. Routes are defined by the sequence of segments, and thus monuments, that they pass through; routes with the same origin and destination are unique unless they go through the same segments in the same order. Exhibit 1-9 shows an example of two routes.

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Exhibit 1-9Routes

An important issue related to the routes is the challenge of creating route travel times by aggregating the segment travel times. It would be ideal if the sample size for each route (origin-destination pair) was sufficient to develop a PDF for every possible departure time under every possible condition, but this is an unreasonable expectation. Hence, it is necessary to aggregate segment travel times to create route-level PDFs. The question is how.

Creating route-level PDFs is not trivial because correlation exists. Many of the same vehicle drivers that create the travel times on one segment are also involved in creating travel times on others, so the travel times for segments nearby one another are clearly related. One cannot assume that the PDFs for travel times (or travel rates) are independent. Consequently, convolution and similar methods cannot be used to combine the PDFs.

The authors of this guidebook elected to use Monte Carlo simulation to create the route travel times. Monte Carlo simulation is commonly used to develop density functions for systems that involve correlation. The critical task is to determine what type of correlation exists and then account for it. Details about how this is done are presented later in this chapter, but in essence the process can be captured by five thoughts:

1. Drivers have desired speeds (or travel rates) at which they want to move. They will navigate their way through the traffic stream to get as close to this desired speed (travel rate) as possible.

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2. The operating conditions on each segment transform this desired distribution into an actual distribution of travel rates for that segment. When the traffic flows are light, the density function is close to that of the desired speeds (rates). When the flows are heavy, the distribution is very different.

3. Correlation (typically positive) exists between the travel rate distributions on adjacent segments because many of the same drivers are involved.

4. Incidence matrices provide a way to account for those correlations. An incidence matrix in this context indicates how many vehicles that had travel rates between τx and τy on the upstream segment will experience travel rates between τr and τs on the downstream segment. That is, they tie the vehicle travel rates on upstream segments to those on downstream segments.

5. Macro-level network flow dynamics determine how the conditions on downstream segments (e.g., increasing or decreasing congestion) affect the traffic conditions on upstream segments (as is always the case).

USER CONCEPTS

The remaining set of ideas have to do with the measure for which the reliability information is being developed: what users are of interest, what geographic definitions are being used for the origins and destinations, what routes and segments are involved, and what conditions pertain.

Users

The users of interest are the entities making the trips. They need to have the same or a similar trip-making behavior. They can be people, vehicles, packages, trucks or some other logical entity. An example would be commuters making weekday trips from zone A to zone B. Another would be vehicles traveling from zone A to the airport. The analyst needs to determine how “similar” these users need to be for them to be classified as a single group; or how heterogeneous they can be without undermining the usefulness of the travel time reliability information.

Route Bundle

A route bundle is a set of two or more routes. The routes in a bundle can have the same monument pair as the start and end points (a set of different paths for the same point-to-point pair) or they can be for slightly different monument pairs, as might be the case for all the routes leading from the monuments in one zone (e.g., a zip code) to all the monuments in another zone. The majority of the intervening segment sequences might be largely the same (one general route) or varied (several general routes exist). Hence, the bundles arise because a) there are many routes or b) there are many originating or terminating monuments, or c) both.

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Exhibit 1-10Route Bundles

Market

A market is formed by a set of users in combination with a route bundle. An example would be commuters who have toll tag equipped vehicles (user group) that travel in the morning (time period) from a suburban area to downtown destination (using a specific route bundle).

Condition

A condition is the system state that exists. An example might be the weekday morning peak on normal workdays. Another might be the state of the system for trips starting between 8:00-8:05 on a typical weekday morning. A third could be Friday evening peaks for major holidays. The important point is that the conditions need to be the system states that are most critical to study, manage, and improve from the operating agency’s perspective. They are likely to be different for each operating agency; and the ones that are important are likely to evolve over time as the agencies fix problems and develop more insight into travel time reliability. Akin to the development of signal timing plans, the system manager will determine over time what conditions are the most important ones to monitor.

Sample Space

The sample space (observation set, observation timeframe, sample frame) is the raw data from which the PDF is being developed. A very detailed example would be travel times for

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vehicles going from A to B using path P starting at 8:00am every Tuesday workday for N years. A more general example would be all the vehicles going from interchange X to interchange Y during the AM peak on winter days across M years. The definition of this sample space is very important. It is likely to be predicated on a time span (e.g., years, months, weeks), a geographic area (e.g., the corridor along a specific freeway), and a set of observation technologies (e.g., single loop point detectors). The nature of this observation set determines what can be learned directly from the data and what additional inferences have to be made to develop other metrics of interest.

COMMUNICATING TRAVEL TIME RELIABILITY

There are significant questions about how users understand travel time reliability information when it is communicated in different ways. There are also questions about how users receive value from the information transferred. This guidebook touches on this topic but defers to SHRP2 Project L14 for guidance on how to ascertain how one should communicate TTR information to end users and other stakeholders. Rather, this guidebook aims to ensure that a set of TTR measurement methodologies and data analysis tools exist that will cover the spectrum of quantitative data that may be or will be needed to convey TTR information to the end users and other stakeholders based on the L14 findings. The methodologies in this guidebook have been fine-tuned to support the L14 findings.

A conclusion of this guidebook is that this objective of supporting the communication metaphors can be satisfied by preparing PDFs for various contexts such as network segments, point-to-point routes, and origin-destination pairs. It is assumed that a PDF prepared based on the methodologies presented in this guidebook will contain the necessary information for a monitoring system to provide the most useful communications to users, as determined by the findings of Project L14.

One way to present information about segment reliability is a consistency map. Although it can be confused with a speed map, it shows which links in the network are experiencing high variability in travel time. The map does not speak to specific origin–destination pairs, but it does help travelers, and route guidance systems, avoid selecting routes that include segments with highly variable travel times. Such a map is shown in Exhibit 1-11 based on data from the San Francisco Bay Area. As might be expected, the links that are displayed in green are providing consistent travel times, while those shown in red are not.

Exhibit 1-11Large Variations in Travel Time in San Francisco Source: BTS

Several reliability measures are in common use today. They have more to do with consistency than reliability, but they are useful metrics:

Planning Time (95th Percentile Travel Time): This represents the average trip duration in minutes and seconds for 95 percent or less of all trips. This measure estimates how bad delay will be during the heaviest traffic days.

Buffer Index: This represents the extra time (in minutes or as a ratio) that travelers must add to their average travel time when planning trips to ensure on-time arrival 95

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percent of the time. This is computed as the difference between the 95th percentile travel time and the average time travel, divided by the average travel time. The buffer index increases as reliability worsens.

Travel Time Index: This is computed as the ratio of the average travel time in the peak period to the travel time at free flow conditions.

Planning Time Index: This is computed as the ratio of the 95th percentile travel time in the peak period to the travel time at free flow conditions. The planning time index can also be understood as the ratio of travel time on the worst day of the month compared to the time required to make the same trip at free-flow speeds. Consequently, the planning time index represents the total travel time that should be planned when an adequate buffer time is included.

Skew Statistic: Computed as the ratio of the difference between the 90th percentile and median travel times and the difference between the median and 10th percentile travel times.

Misery Index: Computed as the difference between the average of the travel times for a percentage (typically 0.5 to 5 percent) of the trips of longest duration and the average travel time, normalized by the average travel time.

Failure/On-Time Measure: Computed as the percent of trips with travel times less than the product of a calibrated factor (e.g., 1.3) and the mean travel time.

Four of these reliability measures are illustrated in Exhibit 1-12.

Exhibit 1-12Graphical Illustration of Selected Reliability Measures Source: Traffic Congestion and Reliability: Trends and Advanced Strategies for

Congestion Mitigation, September 1, 2005, available at http://ops.fhwa.dot.gov/congestion_report/.

The reliability metrics commonly being suggested today, like the 90th or 95th percentile travel time, the buffer index, the planning time index, and the percentage of trips “on time,” are slightly different from but closely related to the discussion about travel time reliability presented earlier in this chapter (see for example Lomax et al., (7), Turner (8) and the FHWA website: http://ops.fhwa.dot.gov/publications/tt_reliability/ (9)). Of the metrics listed on the FHWA website, the closest is the planning time index which computes the ratio of the 95th percentile travel time to an “ideal” or free-flow travel time. Although the 95th percentile is quite volatile, the metric matches well with ideas about consistency and reliability because the basis of comparison is a fixed travel time; it is not referenced to the mean. It has a clear way to determine if failure has occurred. The metric “percentage of trips completed on-time” is also closely aligned if the on-time metric is fixed, by using a DTT (not the average).

The buffer index is more distant because it computes the ratio of the time above the mean up to the 95th percentile divided by the mean. Failure is ambiguous because the target floats, affected by the ever-changing value of the average travel time. Based on the buffer index, reliability can be achieved even if the average travel time is creeping upwards or if it varies widely from one day to the next or one time period to the next. Measures like the buffer index that are based on the variance in the travel times focus more on consistency. They are clearly

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useful in assessing system performance, ascertaining what actions to take to help improve TTR, but they are at odds with the more generally used notion of reliability, as articulated by Ebeling (3) and many others. There have been researchers whose investigations have focused on ideas closer to Ebeling’s. For example, see illustrations in a transportation context from List et al. (10), List et al. (11), Nozick et al. (12), Danielis, Marcucci and Rotaris (13), Kwon, Martland, Sussman and Little (14), Tracey and Tan (15), Chan, Tang, Lau, and Ip (16), McCormack and Hallenbeck (17), and Kraft (18).

PURPOSE OF THE GUIDEBOOK

TRAVEL TIME RELIBILITY MONITORING SYSTEM STRUCTURE

The purpose of the guidebook is to describe how a travel time reliability monitoring system might be constructed. For clarity, the guidebook is generally organized around the structure of a hypothetical travel time reliability monitoring system, shown below in Error: Reference source not found. Each module is shown as a box while the inputs and outputs are shown as circles.

Exhibit 1-1Reliability Monitoring System Overview

GOALS

A number of goals have been established for the monitoring system. These goals can roughly be divided into six categories:

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User Needs

Provide real-time information to satisfy the users currently on the system and archival information to satisfy those planning future trips.

Seamlessly distribute reliability information to system users: public, private, and others, for freight and person trips.

Clearly support the traffic monitoring needs of the public and, to the extent practical, the private sector.

Support FHWA efforts to develop real-time data about the National Highway System, major arterials in metropolitan areas, and transit systems.

Data Collection

Seamlessly accommodate new detection and communication technologies. Have ready capacity for huge volumes of public and private data. Blend existing infrastructure and software with new monitoring technologies and

communication devices. Seamlessly link information sharing between agencies.

Data Management

Monitor real-time equipment malfunction. Identify, report, and correct bad data. Impute travel times and reliability measures for unmonitored areas. Support partnerships between public agencies and private entities to support quality

control and data management.

Computation Engine

Support the developing, testing, and refining of algorithms that predict travel time reliability for real-time and static settings.

Support partnerships between public agencies and private entities to produce effective and usable communication tools.

Report Generation

Support for trend analyses and peer comparisons. Include a broad set of easily understandable performance measures, building upon

those that are already commonly used, that meets the needs of users performing various functions.

Systems Integration

Comply with ITS system architecture requirements. Comply with other standards. Interoperate with legacy systems. Interoperate with other performance monitoring systems.

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Chapters 2 and 3 serve a critical role in the systems engineering process as a communication from the procurers of systems to the developers of systems. The ideas and concepts are intended to explain and explore the potential capabilities of a travel time reliability monitoring system by clearly defining its components.

Agency staff should read this material with an eye toward what is possible and what makes the most sense for their own agency’s travel time reliability monitoring system. For example, from which sources can an agency collect data? Which steps can an agency employ in processing data? What performance measures can an agency realistically calculate? How can information be presented to various end-users? As such, this document can be used as a starting point for an agency to review current practices, recent reliability research, and developing technologies with the aim of designing a system that meets its own needs. Appendix B summarizes some of the key points in this document for agency staff who may be considering procuring a travel time reliability monitoring system.

A functioning reliability monitoring system must meet the needs of many different types of users because different users perceive and value deviations from the expected travel time in different ways. Each of these user classes has different motivations for monitoring travel time reliability, and these needs have to be accounted for in the types of analysis that the system can support through the user interface.

The use cases presented in Chapter 4 guided the preparation of the module descriptions. Use cases are a formal systems engineering construct that transforms user needs into short, descriptive narratives that describe a system’s behavior. Use cases are used to capture a system's behavioral requirements by detailing scenario-driven threads through the functional requirements. The collective use cases define the monitoring system by capturing its functionalities and applications for various users.

Each use case defines the purpose for the analysis, the steps the user must take to obtain the desired data, the results of the steps, the spatial aggregation used, and the travel time reliability measures that can be reported. The measures listed in these use cases are only examples, and represent possible ways that reliability information can be conveyed. The overall theme of the data collection and analysis procedures in this guidebook is that all travel time reliability performance measures can be derived from TT-PDFs. SHRP2 Project L14, scheduled to be completed in 2012, is focused on determining the most effective measures to convey reliability information for various uses. The results of this research effort should reveal the most appropriate measures to support each use case.

ORGANIZATION OF THE GUIDEBOOK

This guidebook is organized into four chapters that provide guidance each of the parts of a travel time monitoring system as presented in Error: Reference source not found:

Chapter 1, Introduction, presents an overview of travel time reliability and this guidebook.

Chapter 2, Data Collection and Management, discusses the types and application of various types of sensors, the management of data from those sensors, and the integration of data from other systems that provide input on sources of unreliability (e.g., weather, incidents, etc.).

Chapter 3, Computational Methods, discusses how probability density functions are derived from the variety of data sources.

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Chapter 4, Applications, presents a discussion of potential use cases for a travel time monitoring system and illustrates its use with a series of real-world case studies.

Travel time monitoring increasingly incorporates a blend of infrastructure from the public and private sectors. As a result, some elements of travel time monitoring systems in current use have proprietary components. This guidebook is not intended to provide a bid-ready specification of all aspects of a travel time monitoring system, some aspects of which may be proprietary. However, the reader will be able to better understand the functional specification of such a system, defined by the required inputs and outputs of such a system.

REFERENCES

1. Transportation Research Board. Strategic Highway Research Program 2, Reliability Focus Area, Overview. June 17, 2009. http://onlinepubs.trb.org/onlinepubs/shrp2/RRPJune2009.pdf. Accessed July 14, 2010.

2. Elefteriadou L., and X. Ciu, “Review of Definitions of Travel Time Reliability,” Proceedings, 86th Annual Meeting of the Transportation Research Board, paper #07-1675 (on CD), Washington, DC, January 21-25, 2007.

3. Ebeling, C.E., Introduction to Reliability and Maintainability Engineering, Mc-Graw-Hill, 1997.

4. Hansson, S.O., Decision Theory: A Brief Introduction, Department of Philosophy and the History of Technology, Royal Institute of Technology, Stockholm, Sweden, 2005.

5. Noland, R.B., K.A. Small, P.M. Koskenoja, and X. Chu, “Simulating travel reliability,” Regional Science and Urban Economics Vol. 28, No. 5, pp. 535-564, 1998.

6. Maginn, J.L., D.L. Tuttle, D.W. McLeavey, and J. E. Pinto, Managing Investment Portfolios: A Dynamic Process: CFA Institute Investment Series, John Wiley & Sons, 3rd Edition, 2007.

7. Lomax, T, D. Schrank, S. Turner, and R. Margiotta, Selecting Travel Time Reliability Measures, Texas Transportation Institute, 2003, http://tti.tamu.edu/documents/474360-1.pdf, Accessed July 7, 2010.

8. Turner, S., Travel Time Reliability Measures, NTOC Web Cast: Travel Time Reliability, June 28, 2006 http://www.ntoctalks.com/webcast_archive/to_jun_28_06/to_jun_28_06_st.ppt, Accessed July 7, 2010.

9. FHWA website: http://ops.fhwa.dot.gov/publications/tt_reliability/).10. List, G.F., B. Wood, M.A. Turnquist, L.K. Nozick, D.A. Jones, and C.R. Lawton,

“Logistics Planning under Uncertainty for Disposition of Radioactive Wastes”, Computers & Operations Research, 33:3 , pp. 701-723, March 2006.

11. List, G.F., B. Wood, L.K. Nozick, M.A. Turnquist, D.A. Jones, E.A. Kjeldgaard, and C.R. Lawton, “Robust Optimization for Fleet Planning under Uncertainty”, Transportation Research, Part E 39E:3, pp. 209-227, May 2003.

12. Nozick, L.K., G.F. List, and M.A. Turnquist, “Integrated Routing and Scheduling in Hazardous Materials Transportation,” Transportation Science, 31:3, pp. 200-215, August 1997.

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13. Danielis, R., E. Marcucci, and L. Rotaris, “Logistics managers’ stated preferences for freight service attributes,” Transportation Research Part E: Logistics and Transportation Review, 41:3, pp. 201-215, 2005.

14. Kwon, O.K., C.D. Martland, J.M. Sussman and P. Little, “Origin-to-destination trip times and reliability of rail freight services in North American railroads,” Transportation Research Record 1489, pp. 1-8, 1995.

15. Tracey, M., and C.L. Tan, “Empirical analysis of supplier selection and involvement, customer satisfaction, and firm performance,” Supply Chain Management: An International Journal, 6:4, pp. 174-188, 2001.

16. Chan, F.T.S., N.K.H. Tang, H.C.W. Lau, and R.W.L. Ip, “A simulation approach in supply chain management,” Integrated Manufacturing Systems, 13:2, pp. 117-122, 2002.

17. McCormack, E., and M.E. Hallenbeck, “ITS Devices Used to Collect Truck Data for Performance Benchmarks,” Transportation Research Record 1957, pp. 43-50, 2007.

18. Kraft, E.R., “Priority-based classification for improving connection reliability in railroad yards. Part II of II: Dynamic block to track assignment,” Transportation Quarterly, 56:1, pp. 107-119, 2002.

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