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Value of Travel Time Reliability

Synthesis Report &

Workshop Working Paper

Prepared for:

SHRP 2 Workshop on the Value of Travel Time Reliability

Prepared by:

Cambridge Systematics

ICF International

April 26, 2012

DRAFTExecutive Summary

To support the SHRP2 Value of Travel Time Reliability Workshop, many national and international research papers were reviewed and synthesized. These documents demonstrate the rich intellectual dialogue and analytical rigor developing around the topic of how to value travel time reliability when making transportation investment decisions. The topic is timely. Transportation agencies across the country are trying to make maximum use of their limited revenues. Evaluating the trade-offs between operational and capacity improvements is an increasingly important part of the plan, program and project decision making processes. Understanding how users of the system --- both passenger and freight --- consider travel time reliability is an important element of evaluating that trade-off.

This pre-workshop synthesis was undertaken with two primary objectives: 1) to identify what concepts and analytical techniques related to the value of travel time reliability are ready to be moved into application; and 2) what topics or areas have gaps in knowledge that need further exploration through research, pilot projects or other methods before broad application is advisable.

This synthesis supports the following findings:

• Past studies of reliability valuation start with defining reliability, then determining how travelers value the components of travel time.

• Travel time reliability has been defined in a variety of ways, most of them closely related. Two concepts have emerged: (1) reliability as the variability in travel times and (2) reliability as the proportion of successes or failures against a pre-established threshold travel time, e.g., on-time arrivals compared to a schedule. Regardless of the definition used, both concepts can be explained in terms of the travel time distribution.

• Two approaches have been used in past studies to define reliability for valuation studies:

o Mean-variance – which uses statistical measures to separate out the value of typical/usual travel time (VOT; the mean or other measure of central tendency) and the value of reliability (VOR; measures for the dispersion of the travel time distribution, such as the standard deviation)

o Schedule Delay – which focuses on the magnitude of the time embodied by both early and late arrivals in relation to a pre-determined schedule

The mean-variance approach is easy to implement in existing analysis frameworks. However, there is concern that the mean value may include a portion of the reliability component, leading to double counting of benefits when analyzing an improvement. Several researchers have indicated their preference for the schedule delay approach on conceptual grounds, but it is difficult to implement for the highway mode where travelers schedules are not known and would vary widely if they were.

• Three methods have been used to determine the value of time (VOT) and (VOR) analytically:

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o Stated preference surveys – where respondents are asked to explain their current traveler behavior and/or how they would react to hypothetical travel situations

o Revealed preference surveys – where actual travel behavior is observed and related to field measurements of travel time

o Options theoretic approach – where the reliability is considered to be a sort of “insurance,” represented by a reduced, guaranteed speed that motorists would be willing to accept in exchange for insurance that speeds would never fall below the guaranteed value. Instead of paying the premium with money, the premium is paid in the form of travel time

• Studies estimating the VOR are not as plentiful as studies estimating the value of time VOT. Although there is still not a unanimous concession on the VOT, there is a long history using it in economic evaluations.

• The Reliability Ratio, the ratio of the VOR divided by the VOT, is a convenient way of estimating the VOR for economic evaluations. If the Reliability Ratio can be established beforehand and the VOT is known, VOR can be computed.

• Past studies of reliability valuation for passenger travel have found a wide range of values, but the more recent studies appear to be coalescing around a Reliability Ratio of 1.0.

• Many non-U.S. countries have undertaken their own review of reliability valuation and have recommended specific values for VOR and/or the Reliability Ratio for use in economic analyses:

o Netherlands: Reliability Ratios of 0.8 and 1.4 for personal auto and public transit, respectively. (Being updated; United Kingdom may adopt the updated number)

o New Zealand: Reliability Ratio of 0.8 for personal autos

o Australia: Reliability Ratio of 1.3 for personal autos

o Sweden: Reliability Ratio of 0.9 for all trip types

o Canada: Transport Canada study recommended a Reliability Ratio of 1.0

• Use of a single (composite) Reliability Ratio in technical analyses may be misleading. Researchers have noted that just as for the VOT, the VOR can vary by a number of factors. SHRP 2 Projects C04 and L04 derived an expansive set of Reliability Ratios for combinations of trip type, income, and trip length. In general, the influence of these factors are:

o Trip type – the Reliability Ratio for the trip to work is higher than the trip from work or non-work trips

o Income – for the work trip, lower income groups have a higher Reliability Ratio (presumably because their work schedules are more rigidly fixed by employers)

o Trip length – for the work trip, the Reliability Ratio decreases with trip distance

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• Studies of how freight users value reliability are not as plentiful as for passenger travel. Some evidence exists that both the VOR and Reliability Ratio is higher than for passenger travel, but these values are highly dependent on the type of commodity.

• A framework for applying reliability valuation in economic analyses requires:

o A measure for travel time reliability. Although several measures are available, analysts will be confined to the ones used in the studies that establish the value of reliability

o A value for reliability

o A method for predicting future reliability that is also sensitive to the changes in reliability due to an improvement. Currently, several options are available for reliability prediction and SHRP 2 Projects L04, L08, C10A, and C10B will provide additional methods

This paper and its associated appendices provide the discussion and analysis to support these findings. The final section, Workshop Next Steps, summarizes the authors’ thoughts on topics and issues that are ripe for discussion during the Value of Travel Time Reliability Workshop.

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1. Introduction

1.1 Purpose The purpose of this synthesis is to provide a starting point for discussing how travel time reliability should be valued for use in economic analysis of transportation investments and in the modeling of traveler behavior, as well as to demonstrate that reliability is a separate category of user benefits that should be included in assessments of transportation projects. The ultimate goal of this work is to improve from the practitioner’s point of view the use of travel time reliability in the standard practices used by transportation agencies.

The valuation of reliability implies that transportation agencies create more value than they have traditionally realized when they invest to reduce congestion. Usually, only the value of time saved is used in calculating benefits. If there is a benefit from the value of reliability, which is additive, then investments have been undervalued. The value of reliability is most apparent for traffic operations strategies that directly address the disruptions that cause unreliable travel.

The synthesis covers the following topics:

• Theoretical considerations for the value of reliability • Use cases (applications) that could utilize the value of reliability • Summary of past research on the value of reliability • A proposed framework for integrating travel time reliability into transportation analyses

that make economic assessments

1.2 Travel Time Reliability: Concepts and Measurement

Definitions of Reliability It is important for practitioners to understand the how researchers have defined reliability as they have developed the theoretical underpinning for analyzing the value of travel time reliability. Travel time reliability has been defined in a variety of ways by different agencies and researchers over the years. A review of several SHRP 2 projects, some completed, some still underway, was conducted to identify how they defined reliability. Table 1 is a summary of those definitions. A more complete discussion of the definitions from these reports can be found in Appendix A.

Table 1. Reliability Definitions

Study/Organization Definition of Reliability F-SHRP Reliability Research Program “…travel-time reliability can be defined in terms of

how travel times vary over time (e.g., hour-to-hour, day-to-day).” “…reliability is based on the notion of a probability or the occurrence of failure… [F]ailure is defined in terms of how many times the travel-time threshold is exceeded while on-time performance measures how many times the threshold is not exceeded.”

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Project C04 (Improving Our Understanding of How Highway Congestion and Pricing Affect Travel Demand)

“…the level of (un)certainty with respect to the travel time and congestion levels.”

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

“…the reliability of the performance is represented by the variability that occurs across multiple days.”

Project L02 (Establishing Monitoring Programs for Travel Time Reliability)

“…travel time reliability is about travel time probability density functions (TT-PDFs) that allow agencies to portray the variation in travel time that exists between two locations (point-to-point, P2P) or areas (area-to-area, A2A) at a given point in time or across some time interval.”

Project L04 (Incorporating Reliability Performance Measures in Operations and Planning Modeling Tools)

“…reliability is essentially a state of variation in expected (or repeated) travel times for a given facility or travel experience.”

Project L03 (Analytic Procedures for Determining the Impacts of Reliability Mitigation Strategies)

The definition of reliability includes not only the idea of variability but failure (or it’s opposite, on-time) as well.

Project L07 (Identification and Evaluation of the Cost-Effectiveness of Highway Design Features to Reduce Nonrecurrent Congestion)

Used the definition from Project L03.

Project L08 (Incorporating Travel Time Reliability into the Highway Capacity Manual)

Reliability can be defined as: The variability in travel times that occur on a facility or a trip over the course of time; and the number of times (trips) that either “fail” or “succeed” in accordance with a pre-determined performance standard or schedule.

Project L11 (Evaluating Alternative Operations Strategies to Improve Travel Time Reliability)

Travel-time reliability is related to the uncertainty in travel times. It is defined as the variation in travel time for the same trip from day to day (same trip implies the same purpose, from the same origin, to the same destination, at the same time of the day, using the same mode, and by the same route).

Texas Transportation Institute (TTI) Urban Mobility Report

A distinction is made between variability and reliability of travel time. Variability refers to the amount of inconsistency of operating conditions, while reliability refers to the level of consistency in transportation service.

A 2000 AASHTO Report Reliability is the percent of on-time performance for a given time schedule as it applies to freight transportation.

2009 Synthesis (Research on Value of Time and Value of Reliability)

Reliability is the percent of trips that reach their destination over a designated facility within a given travel time (or equivalently, at a given travel speed or higher

Florida DOT Reliability is defined as “the percentage of travel

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that takes no longer than the expected travel time plus a certain acceptable additional time.”

While there is considerable variation in the specific language and complexity of the definitions two concepts emerge as common to nearly all:

1. Reliability as the variability in travel times and

2. Reliability as the proportion of successes or failures against a pre-established threshold travel time, e.g., on-time arrivals compared to a schedule.

Regardless of the specific wording, both concepts can be explained in terms of the travel time distribution. Metrics for measuring reliability can be derived from the travel time distribution.

Does Reliability Have Value? Although there are many definitions of reliability, its absence is what we notice. Interim Planning for a Future Strategic Highway Research Program1 referred to the lack of reliability as “that frustrating characteristic of the transportation system that prompts motorists to allow an hour to make a trip that normally takes 30 minutes because the actual trip time is so unpredictable.” This picture of a frustrated public ensures us that reliable travel time is intrinsically valuable.

Two of the most important values obtained from travel demand studies are the value of travel time (VOT) and the value of travel time reliability (VOR). VOT refers to the monetary values travelers place on reducing their travel time. VOR connects the monetary values travelers place on reducing the variability of their travel time.2 VOT has been long established from a basis in consumer theory where value is related to a wage rate or some portion of it. It is considered one of the largest cost components in benefit‐cost analysis of transportation projects because one of the benefits for travelers in a transportation improvement is the reduction of travel time.3

In contrast, VOR is a relatively new concept. Reliability has most often been considered qualitatively and is associated with the statistical concept of variability.4 However, it is clearly recognized by travelers of all types. Travelers account for the variability in their trips by building in “buffers” as insurance against late arrival. This action implies that the consequence of arriving late is “costly” and should be avoided.5 Efficiency and productivity lost in these buffers or safety margins represent an additional cost that travelers absorb.

Reliability is of sufficient value to transportation system users that empirical studies have demonstrated a willingness to pay for reduced travel time. Variability in the costs which are acceptable to different travelers for different trips suggests that this value is not a one-size-fits-all association.6 The difference in value between users or for the type of use must be quantified to be understood and applied appropriately.

1 NCHRP Report 510, 2000 2 Carrion and Levinson, 2010 3 Concas et al., 2009 4 Kittelson and Associates, 2012 5 Organisation for Economic Co-operation and Development (OECD), 2010 6 Concas et al., 2009

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For the business traveler and freight shippers, time is money. The just-in-time delivery aspect of the present economy implies a high cost associated with an unreliable transportation system and a corresponding value for travel time reliability. Freight providers are a unique category of transportation users in many aspects; however, the value placed on reliability is consistent with or greater than other travelers.

In order to be considered substantially in transportation decision making, it is necessary to provide a quantitative VOR which recognizes these implied or unaccounted for costs that clearly exist. The lack of a commonly acknowledged and supported means to quantify VOR represents a barrier to measuring associated benefits and costs as well as use in analysis to support investment decisions.

A key finding in the SHRP 2 C04 research is that “Improvements in travel time reliability are at least as important as improvements in average travel time.” Research suggests that the Reliability Ratio, or the relationship of VOR to VOT, is increasing. This implies that investments to improve reliability may be equally beneficial to those to reduce typical travel times.

Aside from the individual value placed on reliability by travelers and freight providers, it represents an aspect of congestion “where transportation agencies can make significant gains even as travel demand grows.”7 The data suggests that as much as 50% of highway congestion is due to factors beyond the lack of capacity. Work zones, traffic incidents, weather and special events all contribute to congested roadways. The costs associated with congestion in the U.S. are tallied in the billions. In 2010 congestion caused urban Americans to travel 4.8 billion hours more and to purchase an extra 1.9 billion gallons of fuel for a congestion cost of $101 billion.8 Strategies and policies that help address growing urban congestion clearly add value. In situations of significant congestion, travel time reliability may be even more important (i.e. more valuable) than savings in travel time, particularly when travelers have constrained schedules.9

Measuring Reliability Using reliability as a part of a decision making process requires practitioners to develop some method or technique to measure the consequences of improving or not improving the reliability of the transportation system or segment. From a measurement perspective, reliability is quantified from the distribution of travel times, for a given facility/trip and time period (e.g., weekday peak period), that occurs over a significant span of time; one year is generally long enough to capture nearly all of the variability caused by disruptions. A variety of different metrics can be computed once the travel time distribution has been established, including standard statistical measures (e.g., standard deviation, kurtosis), percentile-based measures (e.g., 95th percentile travel time, Buffer Index), on-time measures (e.g., percent of trips completed within a travel time threshold, and failure measures (e.g., percent of trips that exceed a travel time threshold).

The reliability of a facility or trip can be reported for different time slices, e.g., weekday peak hour, weekday peak period, and weekend. Figure 1 shows an actual travel time distribution derived from roadway detector data, and how it can be used to define reliability metrics. The shape of the distribution in Figure 1 is typical of what is found on freeways – it is skewed toward higher travel times. The skew is reflective of the impacts of disruptions, such as incidents weather, work zones, and high demand, on traffic flow.

7 NCHRP Report 510, 2000 8 Schrank et al., 2011 9 Tseng et al., 2005

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Therefore, most of the useful metrics for reliability are focused on the right half of the distribution; this is the region of interest for reliability. Note that a number of metrics are expressed relative to the free-flow travel time, which becomes the benchmark for any reliability analysis. The degree of (un-)reliability then becomes a relative comparison to the free-flow travel time.

Figure 1. The Travel Time Distribution is the Basis for Defining Reliability Metrics

Once the travel time distribution is established and several other traffic flow variables are known, namely, volume and free-flow speed, a wide variety of reliability metrics can be easily developed. The discussion so far has focused on using travel time as the basic unit of measurement. However, for comparing highway sections of different lengths or for aggregating statistics, normalization is required. Pure travel time can be normalized in one of two ways:

• It can be converted to a travel rate, usually expressed in minutes per mile (the inverse of speed); or

• It can be converted to the Travel Time Index (TTI), which is the measured travel time divided by travel time under ideal or free flow conditions. The TTI is a unitless measure and thus is normalized.

0

50

100

150

200

250

300

350

400

4.5 9.5 14.5 19.5 24.5 29.5Travel Time (in Minutes)

Number of Trips (in thousands)

Free Mean 95th Percentile

99th Percentile

Misery Time

Standard Deviation

Buffer Time

Planning Time

Failure Measure

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2. Use Cases for the Value of Reliability Defining travel time reliability and accepting that it has value is only the first step. In order to incorporate travel time reliability into practice within transportation agencies, it is necessary to provide practitioners with the tools to consider it in the context of the day to day activities that support transportation decision making. Because reliability is of obvious value to travelers, practitioners have begun to add this consideration in planning and cost-benefit considerations. With uncertainty as to exactly “how” to account for reliability, this consideration is primarily qualitative and often incorporated after other quantitative considerations have been completed. In order for travel time reliability to be on a level playing field with other factors, it must be incorporated into the quantitative analyses and tools that support transportation planning, programming and project development. Transportation agencies often receive challenges to their decisions, and for this reason, decisions must be both reasonable and defensible. The research-to-date has validated the need and advanced the understanding of how to quantify reliability; but practitioners need direction on how to apply this research appropriately.

There are three primary uses of the value of travel time reliability:

1. Economic impact – primarily cost-benefit analysis

2. Analysis – travel demand modeling and simulation

3. Outreach – communicating the value to others (both externally and internally)

2.1 Economic Impact A cost-benefit analysis represents one of the most common quantitative methodologies used to support trade-off evaluation in transportation decision making (plan, program and project). European studies identify cost-benefit analysis is a technique that can be used to evaluate and prioritize strategies and ensure that the benefits exceed the costs of providing improved reliability. The European International Transport Forum study identifies cost-benefit as the “best option” for including reliability strategies and treatments in the absence of direct road pricing.10

The value of time is an important element in any cost-benefit analysis of potential investments. The U.S. Department of Transportation (USDOT) has provided various travel time values to be used in evaluating transportation projects. These travel time values vary from 50 to 120 percent of the wage rate, depending on the length and the type of travel (personal or business).11 These standardized values are used to monetize the value of reducing travel time by improving throughput. However, the value of time is only one component of time that needs to be incorporated into technical analyses. There is also a benefit derived from predictable travel time: reliability. Incorporating reliability in a basic cost-benefit analysis requires the identification of data and analyses that can quantify the value of travel time reliability.

These parameters allow a reasonable cost-benefit comparison of potential strategies at a high level as a part of pre-programming studies, sketch planning or for advance decision making preferences. A more specific cost-benefit analysis involves the travel demand model analysis or other economic evaluation tools. The C11 project is currently developing tools that can be used to provide the wider range of economic impacts outside of the selected transportation improvement option. This information not only supports trade-off decision making at the planning level, but provides

10 OECD, 2010 11 USDOT, 2003

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stronger information for project development identification of purpose and need. Additional level of detail in this area will also support a better understanding of the important of reliability to freight travel that has not been developed as fully.

Although there is a growing interest in expanding the traditional benefit-cost analysis to include environmental factors such as pollutant emissions, that topic is not included in this synthesis. The intent here is to support an assessment of the merits of adding reliability as one variable to the current benefit-cost analysis.

2.2 Analysis The most robust means of incorporating travel time reliability into transportation decision making is within the technical analyses that support plan development, project selection and project development. The traditional tools of travel demand models and simulation models require data inputs that do not commonly consider travel time reliability. Adjustments within these analyses are possible to account for the impact of reliability at various levels of sophistication.

Traveler behavioral changes in response to congestion and the cost of travel as well as characteristics based on trip purpose or other factors are key inputs to modeling analysis. Extensive research conducted over the past thirty years has supported significant enhancement of travel demand modeling. Consideration of travel time reliability now must be added.12

The willingness to pay for reduced travel time has been found to vary substantially depending on the characteristics of an individual traveler, as well as the context of the particular trip the traveler is undertaking. Typical modeling analyses in the US have only accounted for a limited amount of this variation, typically segmenting by trip purpose (commute versus non-commute), and income segments. In Europe there has been much more research into systematic variation in VOT, mainly deriving values to use in economic cost-benefit analysis, rather than in forecasting.13

The incorporation of travel time reliability measures in demand models and network simulations models represents a major challenge considering the extensive number of variables that must be considered. Significant progress has been made in quantifying reliability and its impact on travel demand. Some promising attempts include:

o Construction of reliability measures at OD-route level to feed into demand model

o Incorporation of reliability in route choice in (efficient) traffic assignment

o Integration of demand and supply sides incorporating reliability

o Exploration of analytical and explicit (multiple simulations) methods with respect to each reliability source14

The costs of unreliable travel may rival those of congestion considering the delays and secondary effects on other activities that occur. Break down in supply-chains can have significant impacts beyond the dynamics of freight transportation to affect all users.15 The seven elements that account for traffic congestion include six that relate directly to travel time reliability: weather, traffic

12 SHRP Project C04 13 SHRP Project C04 14 Vovsha et al., 2011 15 OECD, 2010

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incidents, special events, work zones and signal timing. The importance of expanding the practitioner’s ability to consider these elements in the existing tools and procedures that support decision making cannot be overstated.

2.3 Outreach There is clear and consistent evidence that travelers place a high value on information that supports travel choices in route, mode and time of travel.16 The freight context of just-in-time delivery is equally evident. These facts, as well as other benefits identified, are useful in convincing those with decision making authority in transportation agencies that reliability must be considered and evaluated.

Individual practitioners involved in Systems Operations and Management (SO&M) have communicated why reliability is important to both travelers and transportation agency decision makers within their state. To date, however, the benefits of reliability have been described qualitatively. Many of the projects authorized in the SHRP2 Reliability Program (and reviewed for this synthesis) have been aimed at developing techniques and methodologies for analyzing the benefit of travel time reliability quantitatively. The Program has also recognized, however, that it is also important to develop a more comprehensive and national approach for communicating benefits, both qualitative and quantitative, to decision makers and travelers, and has funded two projects aimed at developing a coordinated approach to communicating the important of reliability, L17 and L31. These SHRP2 projects have resulted in in the development of a “reliability brand”. In parallel with SHRP2 both AASHTO and FHWA have had initiative aimed at communicating the importance of transportation system reliability. As a part of implementing more quantitative measures it is important to consider how the validity of the methodologies, the complexity of the analyses and quantified benefits that result will be communicated to important constituencies and decision makers.

16 OECD, 2010

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3. Past Studies on the Value of Reliability

3.1 Introduction Estimation of time and delay-related benefits of improved transportation system is normally based on evaluation of time saving attributed to road-users. However, when the transportation system is congested, other delay-related factors, particularly reliability, may be more vital to road-users than the time savings. Reliability of a transportation system raises concerns with the uncertainties characterizing travels and arrival at destinations. Implicit or explicit costs for late arrival may outweigh the benefit of time savings. These costs include loss of income (e.g., hourly workers may have wages deducted), promotion, or loss of job.

Additionally, freight faces similar uncertainties in goods delivery. Travel time reliability is becoming increasing critical to businesses, especially the manufacturing sector as many manufacturers are positioning to adopt “just-in-time” manufacturing processes and other schedule-dependent inventory, assembly and distribution logistics. By having a reliable transportation system or network, a manufacturer can minimize its inventory costs. Therefore, an unreliable transportation system makes it difficult for manufacturer to maximize potential gains in productivity from the use of the transportation system.

3.2 Concepts Used in Valuing Reliability Before a practitioner uses any value or specific analytical technique he/she should understand the fundamental basis and limitations of the underlying research. This section is intended to provide an overview of some of the theoretical underpinnings of research results and findings discussed earlier. This section provides only an abbreviated version of the research approaches and concepts reviewed for this synthesis. Appendix B provides a more complete analysis of these resources.

Early Theoretical Work Early theoretical contribution on traveler reactions to uncertain travel time by Gaver (1968) is based on utility maximization. Gaver’s utility maximization framework demonstrates that commuters (or other travelers with desired arrival time) will depart with a “head start” time, meaning travelers anticipate variance in travel times so they plan their departure a little earlier than if travel times were certain. Polak (1987) adds a concave transformation to Gaver’s linear utility function account for risk aversion, while Bates (1990) also develops a model to account for shifts in earlier departure times as variance increases. Jackson and Jucker (1982) assume that travelers tradeoff the expected travel time against travel-time variance (or standard deviation). Unfortunately, this theory ignores any scheduling costs and does not imply any functional form for the relationship between cost and unreliability.

Small (1982) establishes empirically that scheduling costs play a major role in timing of commuter departures by defining a variable to measure how early or late the commuter is vis-à-vis the official work start time.

The theoretical model of Noland and Small (1995) is considered an extension of both Gaver (1968) and Polak (1987) coupled with Small (1982). It therefore accounts for travelers’ choice of alternative departure times and changing congestion levels. Therefore, this model allows for decomposition of morning commute which is the expected cost of schedule delay, lateness and travel time.

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Approaches to Defining Reliability for Valuation Studies Mean-Variance

Unreliability is measured as the standard deviation (or variance) or other measure of dispersion from the travel time distribution. It is assumed that a decision-maker's objective is to minimize the sum of two terms (both assumed to be sources of disutility): expected travel time, and the travel time variability. While the standard deviation is the most common measure of reliability used in past studies, others have been used as well. These include the interquartile range and differences of percentiles (e.g. 90th percentile and median, 80th percentile and the median). The median has been used to represent the average condition as well.

This approach allows the estimation of the Value of Travel Time Reliability (VOR) (also referred as the Value of Travel Time Variability). This value represents the travelers' monetary weight for reducing variability (i.e. improving reliability). In addition, the Reliability Ratio is defined as the ratio of the value of travel time reliability, and the value of travel time (VOT).17 This ratio permits estimation of the Value of Reliability, especially when only the Value of Travel Time is known.18

Agreement on the most appropriate dispersion measure would benefit application of this approach. However, the dispersion measures assume different types of traveler behavior. If the standard deviation or the interquartile range is used, it is assumed that travelers value both early and later arrivals equally (since the standard deviation covers both sides of the mean.) If the dispersion measure covers only the right side of the travel time distribution (e.g., the 80th percentile minus the median), then it is assumed that travelers only value “lateness”.

A potential problem with the mean-variance approach relates to how it is developed with stated preference or revealed preference surveys (covered in Section 3.3). If the overall mean time is used as the indicator of “typical” or “usual” conditions (for the VOT), then it will include a portion of the variability component. This can lead to double counting benefits when applied. Since the travel time distribution is skewed, using the median instead of the mean can help to control for this, which would be helpful in revealed preference studies where the travel times can be established with field data, but adjusting for it in stated preference studies is more problematic.

Scheduling Delays (with Variability)

In this approach, it is assumed that travelers define their own version of a schedule (“arriving on time” at a destination) and adjust their departure times, routes, and modes accordingly. In the scheduling delay approach, early arrivals can be valued differently than late arrivals. Reliability and scheduling are related concepts. The former refers to the disutility of the inconvenience and possible penalties attributed to the unreliability of travel times. The latter refers to the disutility of arriving either too early or too late, when the traveler has time restrictions (e.g. inflexible vs. flexible schedules).

Recent research has shown equivalence (under certain conditions) between mean-variance and the scheduling delays model (see Fosgerau and Karlstrom (2010)), as well as adding additional contributing factors to potential analyses (see Appendix C). Noland and Polak (2002) offer the following observation:

17 More formally, the Reliability Ratio is the value of a time savings in reliability (e.g., one minute of standard deviation saved), divided by the value of average travel time savings (e.g., one minute of usual or average travel time saved). 18 Transportation Benefit-Cost Analysis: “Travel Time Reliability”

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While [our] analysis suggests an equivalence of the two theoretical approaches (scheduling models versus the mean-variance approach), it appears that in most cases, the scheduling cost formulation captures more of the behavioural reactions of travellers. This hinges to a large extent on the value of the coefficient associated with the probability of late arrival and the time varying nature of recurrent congestion. There may be benefits in combining both approaches. One could specify a schedule delay model that includes an additional term that also captures disutility associated with variability independent of scheduling concerns. Empirical studies have examined this and we discuss these results below.

Theoretical considerations aside, there are also practical differences between the mean-variance and scheduling approaches. As the name implies, the scheduling approach requires knowledge of traveler’s schedules and the distribution of their associated arrival times, items which are usually not available in when forecasting travel conditions.

3.3 Research Methods Used

Survey-Based Methods Econometric analysis favors use of observation data usually related to observed choices, called revealed-preference (RP) data. This data technique underpins early studies in travel time valuation. However, there are numerous problems associated with finding real choice situations with adequate variation to yield very detailed reliable estimates (statistically). The above outlined problems led to an interest in the use of hypothetical choice data, generally called the stated-preference (SP). In SP experiments, respondents are asked to state or indicate (paper or web-based) their preference for route choices with various attributes. The attributes include drive time, congestion, and travel time variability. SP has become the predominant data technique used in most studies relating to valuation of travel time saving and travel time variability. Research has shown that the two techniques can result in different estimates of VOT and VOR. In addition, there are concerns with SP-based studies related to the presentation format and concerns that the values placed on punctuality, early or late arrival, by the researchers may not reflect true population values (see Appendix B).

Options Theoretic Approach (NOTE: the following is a discussion of an approach developed in SHRP 2 Project L11, Evaluating Alternative Operations Strategies to Improve Travel Time Reliability. The discussion is taken directly from a working paper for SHRP 2 Project L17.19 This approach is a radical departure from past reliability valuation research and to date is the only one of its kind. One U.S. transportation agency has adopted this approach for valuing reliability.)20

Well-established techniques exist in economics for estimating the value of an opportunity whose future value is not known with certainty, but can be described in terms of probabilities. A person can purchase an option that gives them the right to exercise an opportunity (e.g., to buy or sell something) at a specific point in time, or up to a specific point in time, depending on the type of option. A car insurance policy is an example of an option—one pays an insurance premium to obtain the opportunity to guarantee that one will avoid paying a significantly larger sum in the

19 Kittelson and Associates, 2012 20 Puget Sound Regional Council, 2010

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unlikely event of an accident. So-called “real options” involve the analysis of things that are not readily traded.

Continuing with the insurance-theme, one can, for example, purchase insurance that a communications satellite will perform at a certain level for a certain period of time, or that one will be covered for unexpected travel expenses due to being delayed as a result of sickness, volcanic eruptions, etc. One can theorize that, if such a product were available, motorists would be willing to pay a premium for “travel time insurance” to compensate them for travel times exceeding a guaranteed level. As this insurance is hypothetical and would involve compensation for time loss rather than a direct monetary loss, it can be considered a type of real option.

The value of hypothetical “travel time insurance” represents the reduced, guaranteed speed that motorists would be willing to accept in exchange for insurance that speeds would never fall below the guaranteed value. Instead of paying the premium with money, the premium is paid in the form of travel time. Under unreliable conditions, motorists would experience a mean travel speed x and would risk that their travel time might occasionally be very long.

With “travel time insurance,” motorists would be provided with a reduced, guaranteed minimum speed of y (y<x). The difference in speeds between the risky x and the guaranteed y can be converted to an increased travel time over the study roadway (i.e., the insurance premium); this travel time can then be converted into a monetary value based on an assumed value of time.

The mathematics of determining this value is derived from options theory in economics. For typical applications, the guaranteed speed is taken as the mean travel time and the length of the option period (“insurance policy”) is taken as the travel time required to travel the length of the roadway facility at the lowest 1% speed. For rare-event applications, the variable used for the guarantee could be event duration or the number of events during the life of the project.

3.4 Summary of Research on the Value of Reliability

Passenger Travel Several of the research studies reviewed for this paper include a Reliability Ratio – a value that might support an analysis of the value of travel time reliability in specific circumstances. Table 1 at the end of this section summarizes the values of reliability for passenger travel that were included in the reviewed research. The emphasis is on showing the Reliability Ratio and the reliability metric or definition in each study. Several recent summaries of past research on the valuation of reliability for passenger travel have assessed these studies:

• The aforementioned white paper from SHRP 2 Project L1721 • An unpublished review conducted for SHRP 2 L05 • A Florida DOT Research Report22 • A forthcoming TRB paper publication by Carrion and Levinson23

21 Kittelson and Associates, 2012 22 Concas et al., 2009

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Of these, the Carrion and Levinson work is the most comprehensive. The reviews were all intended to determine reasonable values for VOR and the Reliability Ratio, in addition to critiquing the theoretical basis of the studies and their methodologies. Figure 2 is taken directly from Carrion and Levinson. They were selective in their choice of studies as they were using them for a meta-analysis. It is interesting that there is less variation among more recent studies, and if the means of each individual study is used, the reliability ratios are grouped in the 0.5 – 1.5 range. Previously, SHRP 2 Project C04 also noted the same range. The SHRP 2 L05 effort more narrowly focused the Reliability Ratio range to 0.9 – 1.25 based on including only the research with the most rigorous methods. The FDOT study recommended a Reliability Ratio range of 0.8 – 1.0, based on their assessment of the most rigorous studies.24

Figure 2. Reliability Ratios from Previous Studies

Source: Carrion and Levinson (2012)

Many non-U.S. countries have undertaken their own review of the issue and have recommended specific values for VOR and/or the Reliability Ratio. Kauppila provides an excellent summary of these:25

• Netherlands: Reliability Ratios of 0.8 and 1.4 for personal auto and public transit, respectively. (Being updated; United Kingdom may adopt the updated number.)

• New Zealand: Reliability Ratio of 0.8 for personal autos • Australia: Reliability Ratio of 1.3 for personal autos • Sweden: Reliability Ratio of 0.9 for all trip types • Canada: Transport Canada study recommended a Reliability Ratio of 1.0

23 Carrion and Levinson, 2012 24 The authors also mentioned that the value could be “as much as three times higher” if strict schedule adherence is required for the trip. 25 Kauppila, 2011

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The reliability information garnered from surveys can be used effectively in travel forecasting procedures. It must be noted that using a single (composite) Reliability Ratio in technical analyses may be misleading. Researchers have noted that just as for the VOT, the VOR can vary by a number of factors. SHRP 2 Project C04 found that the Reliability Ratio varies as a function of trip type (work/nonwork) and income level. SHRP 2 Project L04 extended this work and derived an expansive set of Reliability Ratios for combinations of trip type, income, and trip length. In general, the influence of these factors is:

• Trip type – the Reliability Ratio for the trip to work is higher than the trip from work or nonwork trips.

• Income – for the work trip, lower income groups have a higher Reliability Ratio (presumably because their work schedules are more rigidly fixed by employers).

• Trip length – for the work trip, the Reliability Ratio decreases with trip distance.

The International Transport Forum made similar observations in a recent publication:26

A range of reliability values is required to reflect the different major user groups. It is difficult to generalise about the value of reliability as it will be project, location, user, and time-specific. For one project studied, the value of improvements in reliability were found to be negligible, whereas for another project they were found to add 25% to the welfare benefits of time savings achieved. It is important to recognise the importance of disaggregating user values of reliability — the “granularity” of reliability. Different values are placed on reliability by different network users at different times and for different trip purposes. Therefore, a single monetary value for reliability will be of little, if any, use in project appraisal. Practitioners cannot assume that values used in one study are readily transferable to a project in another situation. It is also important to avoid potential double-counting when factoring reliability into project assessment. This can arise if the standard values of time used to assess average time savings already have an implicit, crude value for reliability incorporated in them.

26 International Transport Forum, Policy Brief: Seamless Transport, April 2012.

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Table 2. Past Research on the Value of Reliability: Passenger Travel

Authors Study Type Reliability Ratio (personal auto use) Reliability Metric/Definition

Brownstone and Small (2003) RP/SP 1.18 90th - 50th Percentile

Ghosh (2001) RP 1.17 90th - 50th Percentile

Li, Hensher, and Rose (2010) SP 0.70 Scheduling approach; standard deviation

Borjesson (2008) SP 1.27 Ratio of sensitivity to standard deviation to sensitivity of the mean

Small et al. (1995) SP 2.30 Standard deviation

Small et al. (1999) SP 2.51 Standard deviation

Small, Winston, and Yan (2005) RP 0.91 75th - 25th Percentile27

Levinson and Tilahun (2008) SP 0.89 90th - 50th Percentile

Carrion and Levinson (2010) RP 0.91 90th - 50th Percentile

De Jong et al. (2007) SP 1.35 Standard deviation

Forsgerau et al (2008) RP 1.00 Standard deviation

Yan (2002) RP/SP 0.97 90th - 50th Percentile

Asensio and Matas (2008) SP 0.98 Scheduling approach; standard deviation

Bhat and Sardesai RP/SP 0.26 Scheduling approach; standard deviation

Senna (1993) SP 0.76 Standard deviation

Black and Towriss (1993) SP 0.55-0.70 Standard deviation

Tilahun and Levinson (2007) SP 1.0 Scheduling approach; difference between actual late arrival and usual travel time

Tseng, Ubbels, and Verhoef (2005) SP 0.5 Scheduling approach; difference between early/late arrival time and preferred arrival time

Koskenoja (1996) SP 0.75 Average schedule delay (late and early)

SHRP 2 C04 (Pub. Pending) RP 0.7-1.5 Standard deviation per unit distance

SHRP 2 L04 (Pub. Pending) RP 0.57-2.69 Standard deviation per unit distance

27 An earlier version of this work used the difference in the 80th and 50th percentiles.

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Freight Travel Studies on the valuation of freight reliability are not as prevalent as for passenger travel. Appendix D provides an overview of all of the freight related studies reviewed for this synthesis. These studies indicate that the freight value of reliability varies by commodity, with bulk commodities having the lowest value. However, as Hamer et al. (2005)28 noted, there is little consensus on what the values of VORs or Reliability Ratios should be. It is true that for highways, the VOT for freight (trucking) is higher than for personal auto use. For example, FHWA’s Highway Economic Requirements System (HERS) model uses a $19.86 VOT for autos and a $36.05 value for combination trucks (2006 dollars). If the Reliability Ratios for freight are equivalent to passenger travel, i.e., around 1.0, then VOR for freight will be higher.

28 Hamer et al., 2005

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4. Incorporating the Value of Reliability into Technical Analyses

The research to date has made significant contributions to understanding the theoretical underpinning for a quantitative representation of the value of travel time reliability. The next step is moving theory into practice. Integrating value of travel time reliability into the technical analysis that support plan, program and project decisions will help both practitioners and policy makers “level the playing field” in considering trade-offs among investment choices. The question, therefore, is whether or not the research to date is sufficient to incorporate a value for reliability in the primary quantitative analytical techniques and tools used to support trade-off decision making. In Section 2 this paper outlined three potential use cases for a value of travel time reliability: cost/benefit analysis, travel demand modeling and micro-simulation modeling. Incorporating the value of travel time reliability into each of these is discussed below.

4.1 Cost/benefit analysis The economic benefits from improved travel time reliability are appearing more commonly in benefit-cost analyses.29 To incorporate travel time reliability in any technical analyses, the following are needed:

• A measure for travel time reliability

• A value for reliability

• A method for predicting future reliability

• A method for estimating changes in reliability due to a project30

Measures for travel time reliability have been extensively covered – they all describe the different aspects of the travel time distribution. The primary remaining issue related to measures is what is the appropriate measure of reliability to use in valuation studies, i.e., what aspect of variability do travelers consider? This issue includes not only the appropriate metric but the conceptual approach: should it be mean-variance, scheduling delay, or options theoretic? For passenger travel, the mean-variance approach is the easiest to implement within existing modeling frameworks. Choice of the appropriate metric is gravitating toward the standard deviation or a measure of “spread” on the upper end of the travel time distribution (e.g., 80th percentile travel time minus the median, 90th percentile travel time minus the median).

The value that travelers place on reliability has proven to be elusive, but sufficient experience exists to allow selection of interim values. Several European countries have adopted this approach. There appears to be a consensus that for passenger travel, highway users value reliability at roughly the same rate as for average/typical travel time (i.e., Reliability Ratio of about 1.0). There is not as strong a consensus for the reliability valuation for freight travel, and the value is likely to vary widely by commodity type. High value, time sensitive commodities should have a reliability value significantly greater than for passenger travel. Bulk commodities, which are not extremely sensitive to delivery times, will have a far less value, but they are not very likely to be shipped over highways.

29 Puget Sound Regional Council, 2010 30 Transportation Benefit-Cost Analysis: “Travel Time Reliability”

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4.2 Methods for Predicting Reliability: Travel Demand Modeling In modeling applications, reliability needs to be treated both as an input to and an output from the process, just as average/typical travel time has always been used. As an input, reliability (and average travel time) is used to predict traveler behavior – the demand side of the problem. As an output, it is a measure of the performance that results from the operation of the transportation system, more specifically, from disruptions (e.g., incidents, work zones, inclement weather) and other failures (e.g., traffic control devices) – this is the supply side of the problem.

Project C10B has developed a method for including reliability in its analysis framework. As of this writing, it has yet to be tested, but the same structure can be used in other applications. A description of this method is included in Appendix E.

As an input, reliability affects travelers’ decisions about trip-making and the choice of destination, mode, and route. It can be thought of as an extra impedance to travel over and above the average travel time generally used in demand models. Note that current model’s definition of average travel time is based solely on recurring (demand and capacity) conditions. Considering reliability means that nonrecurring sources of congestion factor into the process.

The concept of “extra impedance due to unreliable travel” is probably the best way to incorporate reliability into the modeling structure as an input. SHRP 2 Project L04 is using this approach where the impedance on a link can be captured as a generalized cost function that includes both the average travel time and its standard deviation (which is used as the indictor of reliability). Therefore, L04 functions will be used to establish the total link impedance for trip distribution and assignment purposes, if they become available in time. If not, the use of Travel Time Equivalents will be used, as discussed below.

In order to apply this method, a method must exist for predicting the standard deviation of travel time. SHRP 2 Project L03 developed such methods from empirical data, using the Travel Time Index (TTI) as the dependent variable. Appendix E provides the details of this method.

As of this writing, coefficients for the reliability utility function have not been developed by Project L04. An alternate method would be to compute “travel time equivalents” for reliability. For this purpose, empirical results developed by Small, Winston, and Yan (2005) could be applied. They defined unreliability as the difference between the 80th percentile travel time and the 50th percentile travel time and found the value of unreliability to be approximately equal to the value of time.

Florida DOT has also developed a method for predicting reliability based on predictive equations.31 This method was developed primarily for estimation of system-wide reliability, but it can be applied at the corridor level as well.

4.3 Methods for Predicting Reliability: Micro-simulation In addition to the SHRP 2 research, reliability estimation methods have been developed for FHWA’s Integrated Corridor Management (ICM) program using micro-simulation. This approach is based on defining scenarios that represent different combinations of operational conditions and demand. After model calibration, multiple model runs are conducted for each scenario to account for random variability in travel demand and supply conditions. Simulation runs are typically performed for each

31 Elefteriadou et al., 2012

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scenario to obtain statistically representative results. The scenarios are defined via cluster analysis reflecting different operational conditions (including fluctuations in travel demand, location and intensity of incidents, weather events, special events, and work zones). Results are used to calculate the travel time reliability for each scenario by comparing the standard deviation across the scenario-based simulation runs against the average run. A more extensive discussion of the application of the C10B research can be found in Appendix E.

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5. Summary of Findings and Workshop Next Steps

5.1 Summary of Findings • Travel time reliability has been defined in a variety of ways, most of them closely related.

Two concepts have emerged: (1) reliability as the variability in travel times and (2) reliability as the proportion of successes or failures against a pre-established threshold travel time, e.g., on-time arrivals compared to a schedule. Regardless of the definition used, both concepts can be explained in terms of the travel time distribution. Metrics for measuring reliability are derived from the travel time distribution.

• Past studies of reliability valuation start with defining reliability, then determining how travelers value the components of travel time.

• Two approaches have been used in past studies to defining reliability for valuation studies:

o Mean-variance – which uses statistical measures to separate out the value of typical/usual travel time (VOT; the mean or other measure of central tendency) and the value of reliability (VOR; measures for the dispersion of the travel time distribution, such as the standard deviation)

o Schedule Delay – which focuses on the magnitude of the time embodied by both early and late arrivals in relation to a pre-determined schedule

The mean-variance approach is easy to implement in existing analysis frameworks. However, there is concern that the mean value may include a portion of the reliability component, leading to double counting of benefits when analyzing an improvement. Several researchers have indicated their preference for the schedule delay approach on conceptual grounds, but it is difficult to implement for the highway mode where travelers schedules are not known and would vary widely if they were.

• Three methods have been used to determine the VOT and VOR analytically:

o Stated preference surveys – where respondents are asked to explain their current traveler behavior and/or how they would react to hypothetical travel situations

o Revealed preference surveys – where actual travel behavior is observed and related to field measurements of travel time

o Options theoretic approach – where the reliability is considered to be a sort of “insurance”, represented by a reduced, guaranteed speed that motorists would be willing to accept in exchange for insurance that speeds would never fall below the guaranteed value. Instead of paying the premium with money, the premium is paid in the form of travel time

• Studies estimating the value of reliability (VOR) are not as plentiful as studies estimating the value of time (VOT). Although there is still not a unanimous concession on the VOT, there is a long history using it in economic evaluations.

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• The Reliability Ratio, the ratio of the VOR divided by the VOT, is a convenient way of estimating the VOR for economic evaluations. If the Reliability Ratio can be established beforehand and the VOT is known, VOR can be computed.

• Past studies of reliability valuation for passenger travel have found a wide range of values, but the more recent studies appear to be coalescing around a Reliability Ratio of 1.0.

• Many non-U.S. countries have undertaken their own review of reliability valuation and have recommended specific values for VOR and/or the Reliability Ratio for use in economic analyses:

o Netherlands: Reliability Ratios of 0.8 and 1.4 for personal auto and public transit, respectively. (Being updated; United Kingdom may adopt the updated number)

o New Zealand: Reliability Ratio of 0.8 for personal autos

o Australia: Reliability Ratio of 1.3 for personal autos

o Sweden: Reliability Ratio of 0.9 for all trip types

o Canada: Transport Canada study recommended a Reliability Ratio of 1.0

• Use of a single (composite) Reliability Ratio in technical analyses may be misleading. Researchers have noted that just as for the VOT, the VOR can vary by a number of factors. SHRP 2 Projects C04 and L04 derived an expansive set of Reliability Ratios for combinations of trip type, income, and trip length. In general, the influence of these factors are:

o Trip type – the Reliability Ratio for the trip to work is higher than the trip from work or non-work trips

o Income – for the work trip, lower income groups have a higher Reliability Ratio (presumably because their work schedules are more rigidly fixed by employers)

o Trip length – for the work trip, the Reliability Ratio decreases with trip distance

• Studies of how freight users value reliability are not as plentiful as for passenger travel. Some evidence exists that both the VOR and Reliability Ratio is higher than for passenger travel, but these values are highly dependent on the type of commodity.

• A framework for applying reliability valuation in economic analyses requires:

o A measure for travel time reliability. Although several measures are available, analysts will be confined to the ones used in the studies that establish the value of reliability

o A value for reliability

o A method for predicting future reliability that is also sensitive to the changes in reliability due to an improvement. Currently, several options are available for reliability prediction and SHRP 2 Projects L04, L08, C10A, and C10B will provide additional methods

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5.2 Workshop Next Steps One of the primary purposes of the Workshop is to validate what aspects of the value of travel time reliability research are ready to be incorporated into practice. Based on this synthesis there are some questions ripe for additional discussion. Some of these are:

• Why do practitioners need a value of travel time reliability? What are the benefits of incorporating it into their technical analyses and agency decision making? Are the benefits greater or less depending on what decisions are being made (planning, corridor planning, project-level)?

• What is the proper method for measuring reliability for the purpose of valuation? (Past research has relied on two approaches: (1) mean-variance and (2) scheduling delay).

• Assuming acceptance that research supports a Reliability Ratio of 1 for passenger travel what processes or analyses would practitioners use this ratio in practice?

• Based on the research findings would it be better to have a range for a passenger Reliability Ratio? If so what should the range be? What factors or criteria should a practitioner use in determining the value within the range to use?

• Are there risks to using a Reliability Ratio? If yes, what are they?

• Are there institutional barriers to incorporating a Reliability Ratio into practice? If yes, what are they?

• Do technical analyses have to be modified to accommodate the valuation of reliability? If so, how?

• Are we ready to establish any specific value or range of values of a Reliability Ratio for freight?

• What gaps in knowledge remain? What gaps could SHRP2 potentially address given within the time and resources remaining?

• What could FHWA do to support implementation? What AASHTO do to support implementation? Are there other organizations that need to be involved in implementing results of the value of travel time reliability research?

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References

Bates, J. 1990. “Modifying Generalised Cost to Deal with Uncertain Travel Times”, paper prepared for the 17th colloquium Vervoersplanologisch, Speurwerk, Den Haag, November.

Brownstone, D. and K.A. Small. 2003. "Valuing Time and Reliability: Assessing the Evidence from Road Pricing Demonstrations", Transportation Research Part A: Policy and Practice, 39.

Carrion, C. and D. Levinson. 2010. “Value of reliability: High occupancy toll lanes, general purpose lanes, and arterials,” in ‘Conference Proceedings of 4th International Symposium on Transportation Network Reliability in Minneapolis, MN (USA)’.

Carrion, C. and D. Levinson. 2012. Value of Travel Time Reliability: A review of current evidence, January 26.

Concas, Sisinnio and Kolpakov. 2009. Synthesis of Research on Value of Time and Value of Reliability, Report No. BD 549-37, January.

Elefteriadou et al. 2012. Multimodal and Corridor Applications of Travel Time Reliability, Florida DOT, March 30.

Elefteriadou, L. and X. Cui. 2007. “Travel Time Reliability and Truck Level of Service on the Strategic Intermodal System – Part A: Travel Time Reliability,” Final Report, BD-545-48, Florida Department of Transportation, April.

Fosgerau, M. and L. Engelson. 2011. “The value of travel time variance”, Transportation Research Part B , Vol. 45.

Fosgerau, M. and Karlstrom A. 2010. “The value of reliability”, Transportation Research Part B, Vol. 44.

Gaver, D. 1968. “Headstart strategies for combating congestion”, Transportation Science , Vol. 2.

Ghosh, A. 2001. “Valuing Time and Reliability: Commuters’ Mode Choice from a Real Time Congestion Pricing Experiment.” Dissertation at the University of California at Irvine.

Hamer et al. 2005. The value of reliability in transport. Outcomes of an expert workshop, RAND Europe Report TR-240-AVV, Leiden, the Netherlands.

International Transport Forum, Policy Brief: Seamless Transport, April 2012.

Jackson,W. and J. Jucker. 1982. “An empirical study of travel time variability and travel choice behavior.” Transportation Science , Vol. 16.

Jenelius, E., L.-G. Mattsson, and D. Levinson. 2011. “Traveler delay costs and value of time with trip chains, flexible activity scheduling and information,” Transportation Research Part B, Vol. 45.

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Kauppila, Jari. 2011. State-of-Practice in Incorporating Reliability into Cost-Benefit Analysis, presentation given at the 2011 TRB Summer Meeting, http://www.transportationeconomics.org/agendas-and-minutes

Kittelson and Associates. 2012. Draft Guidebook: Placing a Value on Travel-Time Reliability, prepared for Strategic Highway Research Program 2, Project L17 A Framework for Improving Travel-Time Reliability, January.

Noland, R. and K. Small. 1995. “Travel-time uncertainty, departure time choice, and the cost of morning commutes”, Transportation Research Record , Vol. 1493.

Noland, R.B. and J.W. Polak. 2002. Travel time variability: a review of theoretical and empirical issues, Transport Reviews, Vol. 22.

Organisation for Economic Co-operation and Development (OECD). 2010. Improving Reliability on Surface Transportation Networks.

Polak, J. 1987. Travel Time Variability and Travel Departure Time Choice: A Utility Theoretic Approach, discussion paper no. 15. Polytechnic of Central London.

Puget Sound Regional Council, Benefit-Cost Analysis: General Methods and Approach, March 2010.

Schrank, David, Tim Lomax, and Bill Eisele. TTI’s 2011 Urban Mobility Report. Tech. Sept. 2011. Web. <http://mobility.tamu.edu>.

Small, K.A. 1982. The Scheduling of Consumer Activities: Work Trips, American Economic Review, Vol. 72.

Small, K.A., C. Winston, and J. Yan. 2005. Uncovering the Distribution of Motorists’ Preferences for Travel Time and Reliability, Econometrica, 73(4).

Transportation Benefit-Cost Analysis. “Travel Time Reliability.” http://bca.transportationeconomics.org/benefits/travel-time-reliability

Tseng, Y. and E. Verhoef. 2008. “Value of time by time of day: a stated-preference study,” Transportation Research Part B, Vol. 42.

Tseng, Y., B. Ubbels and E. Verhoef. 2005. "Value of Time, Schedule Delay and Reliability. Estimation Results of a Stated Choice Experiment among Dutch Commuters Facing Congestion," ERSA conference.

USDOT. 2003. "Revised Departmental Guidance: Valuation of Travel Time in Economic Analysis," U.S. Department of Transportation.

Vovsha, P., M. Bradley, and H. Mahmassani. 2011. “Value of Travel Time Reliability: Synthesis of Estimation Approaches & Incorporation in Transportation Models.” Presentation for the Transportation Research Board.

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APPENDIX A

Definitions of Travel Time Reliability Below is a complete discussion of the various definitions of travel time reliability that are included in the numerous research reports reviewed for this synthesis and workshop working paper.

In terms of highway travel, the F-SHRP Reliability Research Program defined reliability this way: … from a practical standpoint, travel-time reliability can be defined in terms of how travel times vary over time (e.g., hour-to-hour, day-to-day). This concept of variability can be extended to any other travel-time-based metrics such as average speeds and delay. For the purpose of this study, travel time variability and reliability are used interchangeably. A slightly different view of reliability is based on the notion of a probability or the occurrence of failure often used to characterize industrial processes. With this view, it is necessary to define what “failure” is in terms of travel times; in other words, a threshold must be established. Then, one can count the number of times the threshold is not achieved or exceeded. These types of measures are synonymous with “on-time performance” since performance is measured relative to a pre-established threshold. The only difference is that failure is defined in terms of how many times the travel-time threshold is exceeded while on-time performance measures how many times the threshold is not exceeded.

In recent years, some non-U.S. reliability research has focused on another aspect of reliability – the probability of “failure,” where failure currently is defined in terms of traffic flow breakdown. A corollary is the concept of “vulnerability” which could be applied at the link or network level: this is a measure of how vulnerable the network is to breakdown conditions.

Project C04 (Improving Our Understanding of How Highway Congestion and Pricing Affect Travel Demand) defined reliability as “… the level of (un)certainty with respect to the travel time and congestion levels.” It then used statistical measures, primarily the standard deviation of travel time, as the metrics used in subsequent analyses.

Project C05 (Understanding the Contributions of Operations, Technology, and Design to Meeting Highway Capacity Needs) defined it as “… the reliability of the performance is represented by the variability that occurs across multiple days.”

Project L02 (Establishing Monitoring Programs for Travel Time Reliability) used this definition: It is important to start by observing that travel time reliability is not the same as (average) travel time... …travel time reliability is about travel time probability density functions (TT-PDFs) that allow agencies to portray the variation in travel time that exists between two locations (point-to-point, P2P) or areas (area-to-area, A2A) at a given point in time or across some time interval. It is about estimating and reporting measures like the 10th, 50th, and 95th percentile travel times.

Functionally, Project L02 used the notion developed in Project L03 that reliability can be measured using the distribution of travel times for a facility or a trip.

Project L04 (Incorporating Reliability Performance Measures in Operations and Planning Modeling Tools) used this definition:

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…models formulated in this research is based on the basic notion that transportation reliability is essentially a state of variation in expected (or repeated) travel times for a given facility or travel experience. The proposed approach is further grounded in a fundamental distinction between 1) systematic variation in travel times resulting from predictable seasonal, day-specific, or hour-specific factors that affect either travel demand or network capacity, and 2) random variation that stems from various sources of largely unpredictable (to the user) unreliability.

Project L03 (Analytic Procedures for Determining the Impacts of Reliability Mitigation Strategies) used an expanded definition of reliability to include not only the idea of variability but failure (or it’s opposite, on-time) as well.

Project L07 (Identification and Evaluation of the Cost-Effectiveness of Highway Design Features to Reduce Nonrecurrent Congestion) used L03’s definition.

Project L08 (Incorporating Travel Time Reliability into the Highway Capacity Manual has proposed to define reliability: Travel time reliability relates to how travel times for a given trip and time period perform over time. For the purpose of measuring reliability, a “trip” can occur on a specific segment, facility (combination of multiple segments), any subset of the transportation network, or can be broadened to include a traveler’s initial origin and final destination. The concepts discussed here apply to all of these units, as long as it is travel time over some distance that is being measured. Measuring travel time reliability requires that a sufficient history be present in order to track travel time performance.

There are two widely held ways that reliability can be defined. Each is valid and leads to a set of reliability performance measures that capture the nature of travel time reliability. Reliability can be defined as:

The variability in travel times that occur on a facility or a trip over the course of time; and The number of times (trips) that either “fail” or “succeed” in accordance with a pre-determined performance standard or schedule.32

In both cases, reliability (more appropriately, unreliability) is caused by the interaction of the factors that influence travel times: fluctuations in demand (which may be due to daily or seasonal variation, or by special events), traffic control devices, traffic incidents, inclement weather, work zones, and physical capacity (based on prevailing geometrics and traffic patterns). These factors will produce travel times that are different from day-to-day for the same trip.

Project L11 (Evaluating Alternative Operations Strategies to Improve Travel Time Reliability) defined reliability: Travel-time reliability is related to the uncertainty in travel times. It is defined as the variation in travel time for the same trip from day to day (same trip implies the same purpose, from the same origin, to the same destination, at the same time of the day, using the same mode, and by the same route). If there is large variability, then the travel time is considered unreliable. If there is little or no variability, then the travel time is considered reliable.

The Florida Department of Transportation (FDOT) defines reliability as the percentage of travel that takes no longer than the expected travel time plus a certain acceptable additional time (FDOT,

32 In the economic valuation literature, this concept is referred to as “schedule delay” – the amount of travel time deviation from a fixed value (e.g., a published schedule or appointment time).

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2000). This approach, while clearly defining a threshold of unacceptable variability, relies on the value of median travel time, which may change from year to year and may present difficulties in tracking reliability over time (Elefteriadou and Cui, 2007).33

The Texas Transportation Institute (TTI) Urban Mobility Report makes a distinction between variability and reliability of travel time. Variability is refers to the amount of inconsistency of operating conditions, while reliability refers to the level of consistency in transportation service (2003).

A report by the American Association of State Highway and Transportation Officials (AASHTO) defines reliability as the percent of on-time performance for a given time schedule as it applies to freight transportation (AASHTO, 2000).

A recent travel time reliability report prepared for FDOT defines reliability as the percent of trips that reach their destination over a designated facility within a given travel time (or equivalently, at a given travel speed or higher).34

33 Elefteriadou and Cui, 2007 34 Elefteriadou and Cui, 2007

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APPENDIX B

Concepts Used in Valuing Reliability

Early Theoretical Work Early theoretical contribution on traveler reactions to uncertain travel time by Gaver (1968) is based on utility maximization. Gaver’s utility maximization framework demonstrates that commuters (or other travelers with desired arrival time) will depart with a “head start” time, meaning travelers anticipate variance in travel times so they plan their departure a little earlier than if travel times were certain. Polak (1987) adds a concave transformation to Gaver’s linear utility function account for risk aversion, while Bates (1990) also develops a model to account for shifts in earlier departure times as variance increases. Jackson and Jucker (1982) assume that travelers tradeoff the expected travel time against travel-time variance (or standard deviation). Unfortunately, this theory ignores any scheduling costs and does not imply any functional form for the relationship between cost and unreliability.

Small (1982) establishes empirically that scheduling costs play a major role in timing of commuter departures. Let tw be the official work start time. If a commuter leaves home at time th and the travel time on a particular day is T, then the commuter will arrive early if th+T>tw. Small (1982) defines variable to measure how early or late this is: schedule delay early (SDE) is defined as tw-(th+T) if the commuter is early, and zero otherwise; while schedule delay late (SDL) is (th+T) -tw if the commuter is late and zero otherwise. This scheduling cost function Cs, is postulated as follows:

Ls PSDLSDETC θγβα +++= )()( (1)

Where DL is equal to 1 when SDL ≥ 0 and 0 otherwise. The coefficient α is the cost of travel time, and β and γ are the cost per minute of arriving early and late respectively, and θ is an additional discrete lateness penalty.

The theoretical model of Noland and Small (1995) is considered an extension of both Gaver (1968) and Polak (1987) coupled with Small (1982). It therefore accounts for travelers’ choice of alternative departure times and changing congestion levels. Therefore, this model allows for decomposition of morning commute which are the expected cost of schedule delay, lateness and travel time. The model is derived as:

Ls PSDLESDEETEEC θγβα +++= )()()( (2)

Where E(T) is the expected travel time, E(SDE) is the expected schedule delay early, E(SDL) is the expected delay late, and PL = E(DL) is the lateness probability. Given a specific probability distribution function for the uncertain component of travel time T, this formulation enables the analyst to predict a head start time that the traveler will choose and the resulting value of the expected scheduling cost. Increased variability in travel time T will increase this expected cost because it will increase one or more of the last three terms, the exact mix depending on how the traveler responds in altering the head start time.

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Approaches to Defining Reliability for Valuation Studies Mean-Variance

Unreliability is measured as the standard deviation (or variance) or other measure of dispersion from the travel time distribution. It is assumed that a decision-maker's objective is to minimize the sum of two terms (both assumed to be sources of disutility): expected travel time, and the travel time variability. While the standard deviation is the most common measure of reliability used in past studies, others have been used as well. These include the interquartile range and differences of percentiles (e.g. 90th percentile and median, 80th percentile and the median). The median has been used to represent the average condition as well.

This approach allows the estimation of the Value of Travel Time Reliability (VOR) (also referred as the Value of Travel Time Variability). This value represents the travelers' monetary weight for reducing variability (i.e. improving reliability). In addition, the Reliability Ratio is defined as the ratio of the value of travel time reliability, and the value of travel time (VOT).35 This ratio permits estimation of the Value of Reliability, especially when only the Value of Travel Time is known.36

Agreement on the most appropriate dispersion measure would benefit application of this approach. However, the dispersion measures assume different types of traveler behavior. If the standard deviation or the interquartile range is used, it is assumed that travelers value both early and later arrivals equally (since the standard deviation covers both sides of the mean.) If the dispersion measure covers only the right side of the travel time distribution (e.g., the 80th percentile minus the median), then it is assumed that travelers only value “lateness”.

A potential problem with the mean-variance approach relates to how it is developed with stated preference or revealed preference surveys (covered in the Section 4.3). If the overall mean time is used as the indicator of “typical” or “usual” conditions (for the VOT), then it will include a portion of the variability component. This can lead to double counting benefits when applied. Since the travel time distribution is skewed, using the median instead of the mean can help to control for this, which would be helpful in revealed preference studies where the travel times can be established with field data, but adjusting for it in stated preference studies is more problematic.

Scheduling Delays (with Variability)

In this approach, it is assumed that travelers define their own version of a schedule (“arriving on time” at a destination) and adjust their departure times, routes, and modes accordingly. In the scheduling delay approach, early arrivals can be valued differently than late arrivals. Reliability and scheduling are related concepts. The former refers to the disutility of the inconvenience and possible penalties attributed to the unreliability of travel times. The latter refers to the disutility of arriving either too early or too late, when the traveler has time restrictions (e.g. inflexible vs. flexible schedules).

Recent research has shown equivalence (under certain conditions) between mean-variance and the scheduling delays model (see Fosgerau and Karlstrom (2010)), and also the focus has shifted to heterogeneity, and risk attitudes. Other important contributions are in terms of time-varying early/late penalties (see Tseng and Verhoef (2008), and Fosgerau and Engelson (2011)). Also, time-

35 More formally, the Reliability Ratio is the value of a time savings in reliability (e.g., one minute of standard deviation saved), divided by the value of average travel time savings (e.g., one minute of usual or average travel time saved). 36 Transportation Benefit-Cost Analysis

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varying early/late penalties for chained trips is developed in Jenelius et al (2011). Noland and Polak (2002) offer the following observation:

While [our] analysis suggests an equivalence of the two theoretical approaches (scheduling models versus the mean-variance approach), it appears that in most cases, the scheduling cost formulation captures more of the behavioural reactions of travellers. This hinges to a large extent on the value of the coefficient associated with the probability of late arrival and the time varying nature of recurrent congestion. There may be benefits in combining both approaches. One could specify a schedule delay model that includes an additional term that also captures disutility associated with variability independent of scheduling concerns. Empirical studies have examined this and we discuss these results below.

Theoretical considerations aside, there are also practical differences between the mean-variance and scheduling approaches. As the name implies, the scheduling approach requires knowledge of traveler’s schedules and the distribution of their associated arrival times, items which are usually not available in when forecasting travel conditions.

Research Methods Used

Survey-Based Methods Econometric analysis favors use of observation data which relates to observed choices usually, called revealed-preference (RP) data. This data technique underpins early studies in travel time valuation. However, there are numerous problems associated with finding real choice situations with adequate variation to yield very detailed reliable estimates (statistically). The above outlined problems led to an interest in the use of hypothetical choice data, generally called the stated-preference (SP). In SP experiments, respondents are asked to state or indicate (paper or web-based) their preference for route choices with various attributes. The attributes include drive time, congestion, and travel time variability. SP has become the predominant data technique used in most studies relating to valuation of travel time saving and travel time variability.

Ghosh (2001) and Yan (2002) show that median SP estimates of VOT and VOR are about half the median estimates of RP and the difference is statistically significant. Also, Shires and de Jong (2008) show that SP and joint SP/RP studies produce significantly lower value of travel time savings for commute and other passenger travels. Brownstone and Small (2003) hypothesize that the significant difference between SP and RP estimates may be attributed to exaggeration of travel time losses in RP data due to relatively high stress in high congestion.

There are few concerns with SP based studies. This relates to presentation of reliability attributes in the experiment. There is little agreement on the presentation format for SP experiment. The presentation of the attributes in the survey could lead to varying interpretation by respondents. This could lead to incorrect answers to the survey questions. Additionally, values placed on punctuality, early arrival or late arrival by researches could be may not reflect the true population values. These values are sometimes difficult to convey in an SP experiment.

Options Theoretic Approach

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(NOTE: the following is a discussion of an approach developed in SHRP 2 Project L11, Evaluating Alternative Operations Strategies to Improve Travel Time Reliability. The discussion is taken directly from a working paper for SHRP 2 Project L17.37 This approach is a radical departure from past reliability valuation research and to date is the only one of its kind. One U.S. transportation agency has adopted this approach for valuing reliability.)38

Well-established techniques exist in economics for estimating the value of an opportunity whose future value is not known with certainty, but can be described in terms of probabilities. A person can purchase an option that gives them the right to exercise an opportunity (e.g., to buy or sell something) at a specific point in time, or up to a specific point in time, depending on the type of option. A car insurance policy is an example of an option—one pays an insurance premium to obtain the opportunity to guarantee that one will avoid paying a significantly larger sum in the unlikely event of an accident. So-called “real options” involve the analysis of things that are not readily traded.

Continuing with the insurance- theme, one can, for example, purchase insurance that a communications satellite will perform at a certain level for a certain period of time, or that one will be covered for unexpected travel expenses due to being delayed as a result of sickness, volcanic eruptions, etc. One can theorize that, if such a product were available, motorists would be willing to pay a premium for “travel time insurance” to compensate them for travel times exceeding a guaranteed level. As this insurance is hypothetical and would involve compensation for time loss rather than a direct monetary loss, it can be considered a type of real option.

The value of hypothetical “travel time insurance” represents the reduced, guaranteed speed that motorists would be willing to accept in exchange for insurance that speeds would never fall below the guaranteed value. Instead of paying the premium with money, the premium is paid in the form of travel time. Under unreliable conditions, motorists would experience a mean travel speed x and would risk that their travel time might occasionally be very long.

With “travel time insurance,” motorists would be provided with a reduced, guaranteed minimum speed of y (y<x). The difference in speeds between the risky x and the guaranteed y can be converted to an increased travel time over the study roadway (i.e., the insurance premium); this travel time can then be converted into a monetary value based on an assumed value of time.

The mathematics of determining this value is derived from options theory in economics. For typical applications, the guaranteed speed is taken as the mean travel time and the length of the option period (“insurance policy”) is taken as the travel time required to travel the length of the roadway facility at the lowest 1% speed. For rare-event applications, the variable used for the guarantee could be event duration or the number of events during the life of the project.

APPENDIX C

37 Kittelson and Associates, 2012 38 Puget Sound Regional Council, 2010

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Summary of Research on the Passenger Value of Reliability

Passenger Travel Table C1 summarizes several of the past research studies on the value of reliability for passenger travel. The emphasis is on showing the Reliability Ratio and the reliability definition in each study. Several recent summaries of past research on the valuation of reliability for passenger travel have assessed these studies:

• The aforementioned white paper from SHRP 2 Project L1739 • An unpublished review conducted for SHRP 2 L05 • A Florida DOT Research Report40 • A forthcoming TRB paper publication by Carrion and Levinson41

Table C1. Past Research on the Value of Reliability: Passenger Travel

Authors Study Type Reliability Ratio (personal auto use) Reliability Metric/Definition

Brownstone and Small (2003) RP/SP 1.18 90th - 50th Percentile

Ghosh (2001) RP 1.17 90th - 50th Percentile

Li, Hensher, and Rose (2010) SP 0.70 Scheduling approach; standard deviation

Borjesson (2008) SP 1.27 Ratio of sensitivity to standard deviation to sensitivity of the mean

Small et al. (1995) SP 2.30 Standard deviation

Small et al. (1999) SP 2.51 Standard deviation

Small, Winston, and Yan (2005) RP 0.91 75th - 25th Percentile42

Levinson and Tilahun (2008) SP 0.89 90th - 50th Percentile

Carrion and Levinson (2010) RP 0.91 90th - 50th Percentile

De Jong et al. (2007) SP 1.35 Standard deviation

Forsgerau et al (2008) RP 1.00 Standard deviation

Yan (2002) RP/SP 0.97 90th - 50th Percentile

Asensio and Matas (2008) SP 0.98 Scheduling approach; standard deviation

Bhat and Sardesai RP/SP 0.26 Scheduling approach; standard deviation

Senna (1993) SP 0.76 Standard deviation

Black and Towriss (1993) SP 0.55-0.70 Standard deviation

Tilahun and Levinson (2007) SP 1.0

Scheduling approach; difference between actual late arrival and usual travel time

Tseng, Ubbels, and Verhoef (2005) SP 0.5

Scheduling approach; difference between early/late arrival time and preferred arrival time

39 Kittelson and Associates, 2012 40 Concas et al., 2009 41 Carrion and Levinson, 2012 42 An earlier version of this work used the difference in the 80th and 50th percentiles.

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Authors Study Type Reliability Ratio (personal auto use) Reliability Metric/Definition

Koskenoja (1996) SP 0.75 Average schedule delay (late and early)

SHRP 2 C04 (Pub. Pending) RP 0.7-1.5 Standard deviation per unit distance

SHRP 2 L04 (Pub. Pending) RP 0.57-2.69 Standard deviation per unit distance

Of these, the Carrion and Levinson work is the most comprehensive. The reviews were all intended to determine reasonable values for VOR and the Reliability Ratio, in addition to critiquing the theoretical basis of the studies and their methodologies. (Carrion and Levinson went as far as to conduct a formal meta-analysis, though they found it inconclusive.)

Figure C2 is taken directly from Carrion and Levinson. They were selective in their choice of studies as they were using them for a meta-analysis. It is interesting that the more recent studies show less variation between them, and if the means of each individual study is used, the reliability ratios are grouped in the 0.5 – 1.5 range. Previously, SHRP 2 Project C04 also noted the same range. The SHRP 2 L05 effort more narrowly focused the Reliability Ratio range to 0.9 – 1.25 based on including only the research with the most rigorous methods. The FDOT study recommended a Reliability Ratio range of 0.8 – 1.0, based on their assessment of the most rigorous studies.43 Tseng (2010) conducted a meta-analysis of past studies and found that SP-based studies produced lower estimates of the Reliability Ratios than those based on RP methods: 0.9477 versus 0.6375.

The reliability information garnered from surveys can be used effectively in travel forecasting procedures. For example, SHRP 2 Project C04, Improving Our Understanding of How Highway Congestion and Pricing Affect Travel Demand, developed a conceptual utility function for traveler choices that includes reliability (defined by the standard deviation):

U= a + b × MedianTime + c × Cost/(Ince ×Occf)+ d × SDevTime/Dist + …. Where: a is a an alternative-specific “bias” constant for tolled facilities

b is the travel time coefficient, ideally estimated as a random coefficient to capture residual heterogeneity

MedianTime is the median, typical expected, travel time by auto

c is the monetary cost coefficient

Cost/(Ince ×Occf) is the monetary cost, scaled by power functions of both income and vehicle occupancy

d is the reliability coefficient

SDevTime/Dist is a measure of travel time reliability, specified as the day-to-day standard deviation of the travel time by auto, divided by distance

And: Value of Time, VOT = b/c

Value of Reliability, VOR = d/c

Reliability Ratio, VOR/VOT = d/b

43 The authors also mentioned that the value could be “as much as three times higher” if strict schedule adherence is required for the trip.

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Figure C2. Reliability Ratios from Previous Studies

Source: Carrion and Levinson

Many non-U.S. countries have undertaken their own review of the issue and have recommended specific values for VOR and/or the Reliability Ratio. Kauppila provides an excellent summary of these:44

• Netherlands: Reliability Ratios of 0.8 and 1.4 for personal auto and public transit, respectively. (Being updated; United Kingdom may adopt the updated number.)

• New Zealand: Reliability Ratio of 0.8 for personal autos • Australia: Reliability Ratio of 1.3 for personal autos • Sweden: Reliability Ratio of 0.9 for all trip types • Canada: Transport Canada study recommended a Reliability Ratio of 1.0

It must be noted that using a single (composite) Reliability Ratio in technical analyses may be misleading. Researchers have noted that just as for the VOT, the VOR can vary by a number of factors. SHRP 2 Project C04 found that the Reliability Ratio varies as a function of trip type (work/nonwork) and income level. SHRP 2 Project L04 extended this work and derived an expansive set of Reliability Ratios for combinations of trip type, income, and trip length. In general, the influence of these factors is:

• Trip type – the Reliability Ratio for the trip to work is higher than the trip from work or nonwork trips.

44 Kauppila, 2011

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• Income – for the work trip, lower income groups have a higher Reliability Ratio (presumably because their work schedules are more rigidly fixed by employers).

• Trip length – for the work trip, the Reliability Ratio decreases with trip distance.

The International Transport Forum made similar observations in a recent publication:45

A range of reliability values is required to reflect the different major user groups. It is difficult to generalise about the value of reliability as it will be project, location, user, and time-specific. For one project studied, the value of improvements in reliability were found to be negligible, whereas for another project they were found to add 25% to the welfare benefits of time savings achieved. It is important to recognise the importance of disaggregating user values of reliability — the “granularity” of reliability. Different values are placed on reliability by different network users at different times and for different trip purposes. Therefore, a single monetary value for reliability will be of little, if any, use in project appraisal. Practitioners cannot assume that values used in one study are readily transferable to a project in another situation. It is also important to avoid potential double-counting when factoring reliability into project assessment. This can arise if the standard values of time used to assess average time savings already have an implicit, crude value for reliability incorporated in them.

45 International Transport Forum, Policy Brief: Seamless Transport, April 2012.

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APPENDIX D

Summary of Research for Freight Value of Reliability

Freight Travel Studies on the valuation of freight reliability are not as prevalent as for passenger travel. A review of several relevant studies follows. These studies indicate that the value of reliability varies by commodity, with bulk commodities having the lowest value. However, as Hamer et al. (2005) noted, there is little consensus on what the values of VORs or Reliability Ratios should be. It is true that for highways, the VOT for freight (trucking) is higher than for personal auto use. For example, FHWA’s Highway Economic Requirements System (HERS) model uses a $19.86 VOT for autos and a $36.05 value for combination trucks (2006 dollars). If the Reliability Ratios for freight are equivalent to passenger travel, i.e., around 1.0, then VOR for freight will be higher.

De Jong et al (2004), New Values of Time and Reliability in Freight Transport in the Netherlands. Research Project carried out for AVV (Transport Research Centre) of the Dutch Ministry of Transport. Sample size, geographic area, data used: Type of data and year collected. Data was collected through interviews and survey. The survey comprised both stated and revealed-preference. A total of 435 shippers and freight carriers in the Netherlands participated in the study. Estimation Model, variable used: Standard and mixed logit models, Jacknife method. Variables used include transport cost ( rates for shippers that contract out activities to carriers), door-to-door transport time, percentage not delivered on time, probability of damage and frequency of shipment.

Results: Values of reliability

• Road transport for low value raw materials and semi-finished goods = 1.01 Euro per shipment;

• Road transport for high value raw materials and semi-finished goods = 1.31 Euro per shipment;

• Road transport for final goods with loss of value = 2.67 Euro per shipment; • Road transport for final goods without loss of value = 2.51 Euro per shipment; • Road transport for containers = 2.85 Euro per shipment • Road transport for total freight transport = 1.77 Euro per shipment; • Train = 898.081.31 Euro per shipment • Inland waterways barge = 62.53 Euro per shipment; • Sea ship (short and deep sea) = 930.60 Euro per shipment • Aircraft = 15429.36 Euro per shipment.

The above values of reliability measures are associated with 10% change in reliability (measured as the percentage not delivered on-time). Unlike Small et al (2005), this study does not categorize by its time sensitivity. Agricultural products are time sensitive while say, consumer electronic products are not. Therefore each of these freight, though they may travel by road, have different values associated with travel time reliability. Unlike passenger, value of reliability associated with freight transport comprises the likelihood of loss of value of freight, increased transportation cost

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and higher inventory level. Although the authors mentioned it is not clear from the study, the contribution of each of these components in the estimation of the aggregate value of reliability. Consequently the above values of reliability shall be considered as average values of reliability for perishables and non-perishables and by mode.

Fowkes A.S (2007), The design and interpretation of freight stated-preference experiments seeking to elicit behavioral valuations of journey attributes.

Sample size, geographic area, data used: Type of data and year collected. Data was collected through the Leeds Adaptive Stated-preference (LASP) survey. The LASP is a computer-based survey that shows respondents screen containing several alternative ways of moving their goods. A total of 49 interviews were conducted with transport managers between September 2003 and February 2004. The survey provided respondents with four alternatives comprising road and rail, but these were not detailed in the study.

Estimation Model, variable used: Manual method and weighted regression analysis of logit. Variables include cost, on-time reliability, damage risk, security risk, shipment distance.

Results: The author indicates that estimated values for reliability ranges between the value of time and twice those values. Therefore, the recommended values for reliability for the whole sample, and the Bulks and Non-bulks are set double those for the value of time. Sample size of 49 is relatively small. Based on the Central Limit Theorem (CLT), the results could be improved if higher survey participation had been solicited. Additionally, the study is biased towards bulk goods. Although, value of reliability was estimated for non-bulks, there was no detail as to which non-bulks were applicable. Also the study did not separately value reliability by mode. The above reliability values are applicable to bulk freight.

Fowkes, Firmin, Whiteing Freight road users valuations of three different aspects of delay.

Type of data and year collected. Data was collected through the Leeds Adaptive Stated-preference (LASP) survey. The LASP is a computer-based survey that shows respondents screen containing several alternative ways of moving their goods. A total of 40 interviews were conducted with transport managers between September 2003 and February 2004. The survey provided respondents with four alternatives comprising road and rail, but these were not detailed in the study.

Estimation Model, variable used: Manual method and weighted regression analysis of logit. Variables include cost, on-time reliability, damage risk, security risk, shipment distance.

Results: Values of reliability

Table D1.

VDT VSP VSH

Category N p(£0.01)/min p(£0.01)/min p(£0.01)/min

Whole sample 40 107.1 85.3 65.8

Own account 11 169.1 89.5 126

Third party (carrier 19 155.1 167.6 86.8

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interviewed)

Third party (shipper interviewed) 10 37.2 61.5 31.3

Distribution 25 183.6 128.7 104.2

Not distribution 15 76.2 56.9 47.7

J.I.T/QR 27 128.6 101.8 75.9

Not J.I.T/QR 13 61 46.8 25.6

Articulated 33 98.4 90.2 63.4

Not articulated 7 126.6 78.1 74.7

Distance Less Than 250 km 14 89.9 93.8 59

Distance Greater Than 250 km 26 125 74.5 74.1

Chemicals, Chem. Products, Paint 8 224.7 126.6 94.3

Food, Drink, Grocery 15 90.9 77.5 48.4

Other Commodities 17 145.7 93.3 97

Rail Possible 13 77.9 60.4 56.3

Rail Not Possible 27 120.5 96.2 69.6

Daytime Movement Only 32 97.3 72 61.4

Some Night Time Movement 8 431.5 159 173.9

North East Interviewer 18 50.5 104.7 49.2

Huddersfield Based Interviewers 22 131.4 80.3 80

VDT = Value of Delay Time VSP=Average arrival spread

VSH = Schedule Delay

VDT is the delay resulting from an increased journey time, with fixed departure time, VSP is the increase in the spread ( or range ) of arrival times for a fixed departure time and VSH is a schedule delay where the departure time is effectively put back.

Bolis and Maggi, Adaptive Stated-preference Analysis of shippers’ transport and logistics choice.

Sample size, geographic area, data used: Type of data and year collected. Data collected was based on the Leeds Adaptive Stated-preference (LASP) survey. The LASP is a computer-based survey that shows respondents screen containing several alternative ways of moving their goods. Transport and logistics managers (rail) of four firms transporting two commodity groups were surveyed for the study. The survey provided respondents with three alternatives comprising, but were not detailed in the study.

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Estimation Model, variable used: The authors modeled the study using the transport and logistics services as a production function and conceived the firm as a network.. Variables include transport (cost, time and reliability and mode) and logistics (frequency and flexibility).

Results: Sector: chemical, production company, and client both operate JIT, serves client in regional market; value of reliability is Lit 39,008 per hour.

Sector: chemical, production company, serves client in foreign market; value of reliability is Lit 5,066 per hour.

Sample size of four is too small to yield results that are representative of the population. Additionally, the four firms represent two only two sectors.

Bergkvist Erik, Regional valuation of infrastructure improvements. The case of Swedish road freight.

Sample size, geographic area, data used: Type of data and year collected. Data was collected through computer-based stated-preference survey in Sweden in 1992. 277 companies participated in the survey. Companies with less than 10 employees were excluded from the survey. The survey was designed to enable researchers construct a model to forecast companies choices between trucks and other modes based on their attributes.

Estimation Model, variable used: Logit models.. Variables used included: • Door-to-door travel time; • Transport cost; • Percent deviation from arrival time (on same day); • Percent deviation from arrival time (on wrong day); • Per mileage damage.

Results: The study estimated value of time to be 14 SEK and the variance of 22.7SEK.

The composition of the companies by industry is unknown. Therefore the above values would be considered average for freight movements. Therefore RR, defined as ratio of value of travel time reliability to value of travel time is estimated to be 1.62.

Danielis R., Marcucci E., Rotaris L, Logistics managers’ stated-preference for freight services attributes.

Sample size, geographic area, data used: Type of data and year collected. Data was collected through computer-based stated-preference survey. 65 manufacturing firms of different sizes participated in the survey. 35 firms are located in Fruili Veneza Giulia, northeast of Italy bordering with Austria and Slovenia, while the remaining 30 firms are located in Marche, a region in the center of Italy. The survey is a computer-based survey powered by the adaptive conjoint analysis (ACA) software. The first of the two part survey collects generate information about the firm. The second part collects information about a typical transport relation on the input and out sides of the company. The ACA software assigns a utility to responses provided by survey respondents.

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Estimation Model, variable used: following assignment of utilities by the ACA software, ordered probit model was used to model choice of alternatives (road vs. rail). A logit model was first tested but did not provide desirable results. Consequently, additional data were collected in 29 experiments. A fixed effect ordered probit model was estimated. Variables used included cost, travel time, travel time reliability and damage cost

Results: Value of time (VOT) = 7.1 Euro per hour

Value of reliability (VOR) = 9.7 Euro per hour

The study is biased towards the manufacturing sector and the associated value of time and reliability is ridiculously low. Most manufacturing firms are engaged in JIT operations, thus making delivery of input materials and in some cases output materials time sensitive. Therefore an hour of delay in input materials should be in excess of 9.7 Euro. Considering that the hourly wage of a Bus Driver in Italy is 36 Euro (2005), an hour of delay of shipment must be in excess of 36 Euros. It is possible that the firms surveyed manufacture low value goods, do not operate JIT or their clients do not operate JIT. The study did not differentiate between reliability values for durable and non-durable goods.

Wigan et al., Valuing Long Haul and metropolitan Freight Travel Time and Reliability.

Sample size, geographic area, data used: Type of data and year collected. Data was collected through paper-based contextual stated-preference (CSP) survey in Australia In CSP survey, un underlying conjoint design ensures that no alternative is either clearly preferred or inferior to all the others. 43 people. Data was collected on three market segments: Inter-capital full truck load (FTL), Metropolitan FTL and Metropolitan multidrop. And survey respondents were drawn from automotive parts, food and beverages, certain building materials and packaging.

Estimation Model, variable used: NLOGIT, a component of Limdep 7 software was used to analyze the survey. Variables used included travel time, cost, travel time reliability and damage.

Results:

Table D2. Freight Travel time implicit unit values (in 1998 $ AUD) Segment Freight travel time Reliability

Inter-capital(FTL) $0.66 pallet/hour $2.56 per 1% reduction Urban (FTL) $1.30 pallet/hour $1.25 per 1% reduction Metropolitan multi-drop $1.40 pallet/hour $1.97 per 1% reduction

From the result, inter-capital market segment has the least hourly value of time. However it is associated with the highest reliability value. The results show that businesses are willing to pay AuS$2.56 to reduce travel time by one percent.

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APPENDIX E

Incorporating Travel Time Reliability in Travel Demand Forecasting:

SHRP 2 Project C10B

Project C10B has developed a method for including reliability in its analysis framework. As of this writing, it has yet to be tested, but the same structure can be used in other applications. A description of this method follows.

As an input, reliability affects travelers’ decisions about trip-making and the choice of destination, mode, and route. It can be thought of as an extra impedance to travel over and above the average travel time generally used in demand models. Note that current model’s definition of average travel time is based solely on recurring (demand and capacity) conditions. Considering reliability means that nonrecurring sources of congestion factor into the process.

The concept of “extra impedance due to unreliable travel” is probably the best way to incorporate reliability into the modeling structure as an input. SHRP 2 Project L04 is using this approach where the impedance on a link can be captured as a generalized cost function that includes both the average travel time and its standard deviation (which is used as the indictor of reliability). Therefore, L04 functions will be used to establish the total link impedance for trip distribution and assignment purposes, if they become available in time. If not, the use of Travel Time Equivalents will be used, as discussed below.

In order to apply this method, a method must exist for predicting the standard deviation of travel time. SHRP 2 Project L03 developed such methods from empirical data, using the Travel Time Index (TTI) as the dependent variable.

Urban Freeways46

95th %ile TTI = 1 + 3.6700 * ln(MeanTTI) (1)

90th %ile TTI = 1 + 2.7809 * ln(MeanTTI) (2)

80th %ile TTI = 1 + 2.1406 * ln(MeanTTI) (3)

50th %ile TTI = MeanTTI0.8601 (4)

StdDevTTI= 0.71*(MeanTTI - 1)0.56 (5)

46 TTI is the ratio of the actual travel time to the ideal or free flow travel time

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Signalized Arterials

95th Percentile TTI = 1 + 2.6930 * ln(MeanTTI) (6)

80th Percentile TTI = 1 + 1.8095 * ln(MeanTTI) (7)

MedianTTI = MeanTTI0.9149 (8)

StandardDeviation = 0.3692 * (MeanTTI – 1)0.3947 (9)

MeanTTI is the grand (overall) mean – since it was developed from continuous detector data it includes all of the possible influences on congestion (e.g., incidents and inclement weather). Most applications and models that predict mean travel time, speeds, etc., almost always only consider recurring congestion. Therefore, an adjustment must be made to the recurring-only travel time so that it corresponds to the overall mean:

OverallMeanTTI = 1.0274 * RecurringMeanTTI1.2204 (10)

Where: OverallMeanTTI is the MeanTTI in the predictive equations 1-8

RecurringMeanTTI is the mean TTI that considers recurring sources only.

One adjustment needs to be made in Equation 10. The definition of “recurring” was based on times when no disruptions were present (incidents, weather, and work zones). It does include the effect of variable demands from day-to-day. If an estimate of the RecurringMeanTTI from a traffic model is based on typical or average conditions, then this effect it would need to be adjusted (factored up) to meet the definition used in Equation 10. If an estimate of the RecurringMeanTTI is based on something like the 30th highest hour concept, then demand variability is implicitly handled.

A better method of estimating MeanTTI (the overall mean that includes both recurring and nonrecurring sources) is to use the simulation model (DynusT, in the case of Project C10B) output to calculate recurring delay, make an independent estimate of incident delay, then combine the two. The steps are:

1. Compute the recurring delay in hours per mile from the simulation model:

RecurringDelay = AverageTravelRate – (1/FreeFlowSpeed) (11)

2. Compute the delay due to incidents (IncidentDelay) in hours per mile using the lookup tables from the IDAS User Manual47. This requires the v/c ratio, number of lanes, and length and type of the period being studied (e.g., 2-hour peak period).

3. Compute the Overall Mean Travel Time Index which includes the effects of recurring and incident delay:

MeanTTI = (RecurringDelay + IncidentDelay) / (1/FreeFlowSpeed) (12)

Note that since the L03 equations predict the TTI; the travel time can be computed as:

47 IDAS User’s Manual, Appendix B, Tables B.2.14 – B.2.18, http://idas.camsys.com/documentation.htm

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TravelTime = TTI * FreeFlowSpeed (13)

As of this writing, coefficients for the reliability utility function have not been developed by Project L04. An alternate method would be to compute “travel time equivalents” for reliability. For this purpose, we recommend using empirical results developed by Small, Winston, and Yan48. They defined unreliability as the difference between the 80th percentile travel time and the 50th percentile travel time and found the value of unreliability to be approximately equal to the value of time.

Based on this result, we recommend the following equation to calculate travel time equivalents for a trip:

TTE = MTT + a * (80th%TT − 50%TT ) (14)

Where:

• TTE is the travel time equivalent on the link

• MTT is the mean travel time (min)

• “a” is the Reliability Ratio (assumed value is 1.0)

• 80%TT is the 80th percentile travel time (min)

• 50%TT is the 50th percentile travel time (min)

MTT, 80%TT, and 50%TT are computed as shown in Appendix B. The “a” parameter reflects the value of unreliability relative to mean travel time (based on currently available information, we recommend a value of 1.00 for this parameter but this value may be revised based on future research).

TTE is then used as a replacement for the average travel time in the feedback loop to the activity model – it is basically an inflated value of travel time over the average that accounts for how travelers value reliability. How the activity model – which was calibrated using average travel time – will behave with this inflated travel time value is unknown and will be the subject of testing.

The above completes the “input” (demand) side of reliability inclusion. To produce estimates of the economic impact of reliability, the following procedure is proposed:

• Compute total equivalent delay based on the TTE:

TotalEquivalentDelay = (TTE - FreeFlowTravelTime) * VMT (15)

Delay may be decomposed into passenger and commercial portions using different travel time equivalents and VMT values.

48 Small et al., 2005

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• Value delay with the usual unit costs for the value of (average) travel time applied to the travel time equivalent. The adjustment for reliability has already been made.

Adapting/Extending the C10B Procedure The methods used in the C10B procedure can be replaced by other analytic methods as they become available. For example, if the analysis deals with an extended highway segment, the Project L08 procedure, when it becomes available, can be used to estimate reliability directly without having to apply the Project L03 and IDAS equations. The FDOT reliability prediction method could also be substituted for the L03 and IDAS equations. Utility functions, such as those being explored in Project L04, that directly accommodate reliability can be used in place of the travel time equivalents approach.