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Improving Our Understanding of How Highway Congestion and Pricing Affect Travel Demand (2012)

Chapter: Chapter 2 - Review and Selection of Data Sources

« Previous: Chapter 1 - Research Objectives and Main Methodology
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Suggested Citation:"Chapter 2 - Review and Selection of Data Sources." National Academies of Sciences, Engineering, and Medicine. 2012. Improving Our Understanding of How Highway Congestion and Pricing Affect Travel Demand. Washington, DC: The National Academies Press. doi: 10.17226/22689.
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Suggested Citation:"Chapter 2 - Review and Selection of Data Sources." National Academies of Sciences, Engineering, and Medicine. 2012. Improving Our Understanding of How Highway Congestion and Pricing Affect Travel Demand. Washington, DC: The National Academies Press. doi: 10.17226/22689.
×
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Page 34
Suggested Citation:"Chapter 2 - Review and Selection of Data Sources." National Academies of Sciences, Engineering, and Medicine. 2012. Improving Our Understanding of How Highway Congestion and Pricing Affect Travel Demand. Washington, DC: The National Academies Press. doi: 10.17226/22689.
×
Page 34
Page 35
Suggested Citation:"Chapter 2 - Review and Selection of Data Sources." National Academies of Sciences, Engineering, and Medicine. 2012. Improving Our Understanding of How Highway Congestion and Pricing Affect Travel Demand. Washington, DC: The National Academies Press. doi: 10.17226/22689.
×
Page 35
Page 36
Suggested Citation:"Chapter 2 - Review and Selection of Data Sources." National Academies of Sciences, Engineering, and Medicine. 2012. Improving Our Understanding of How Highway Congestion and Pricing Affect Travel Demand. Washington, DC: The National Academies Press. doi: 10.17226/22689.
×
Page 36
Page 37
Suggested Citation:"Chapter 2 - Review and Selection of Data Sources." National Academies of Sciences, Engineering, and Medicine. 2012. Improving Our Understanding of How Highway Congestion and Pricing Affect Travel Demand. Washington, DC: The National Academies Press. doi: 10.17226/22689.
×
Page 37

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32 C h a p t e r 2 This chapter describes the main data sources selected and used for the estimation of models that are described in detail in Chapter 3. To support the specification and estimation of advanced modeling components in C04, the team required certain types of information that are typically not available, but that were needed to overcome certain critical data issues surrounding the joint analysis of congestion and pricing. These critical data issues included • Lack of observed data on travel time variability and reli- ability; • Correlation between key time and cost variables; • Bidirectional causality between travel times and demand; • Lack of actual pricing options; and • Lack of validation for stated preference (SP)–based methods. Although a wide range of potential data sets were identi- fied and evaluated in Phase 1, many of them shared the limi- tations listed above. As a result, the decision was made to focus the model estimation work to be done in C04 on the relatively few and most robust of the identified data sources. These are logically grouped by their main characteristics: • Revealed preference (RP) data on travel demand; • Network level of service (LOS) and reliability measures; • SP survey data; and • Experimental travel data. revealed preference Data on travel Demand New York Household Survey The Regional Travel–Household Interview Survey (RT-HIS) that was used to develop the New York Metropolitan Trans- portation Council’s best practice model (NYBPM) was conducted over a 1-year period in 1997 and 1998. Almost 11,000 households were included in the sample obtained from the 28-county tristate (New York, New Jersey, and Con- necticut) NYBPM modeling area. A 1-weekday day travel– activity diary was obtained for each household member. The survey data were recently reweighted and expanded for a base year 2005 update of the NYBPM. While it includes data on amount of tolls paid, it does not include choice of specific tolled facilities or routes. The RT-HIS data, combined with NYBPM network-generated travel time measures, were used directly in the C04 research to estimate congestion and pric- ing impacts with respect to daily activity patterns, mode, occupancy, destination, and time of day (TOD). New York Surveys of Toll Facility Users There are two important recent sets of large-sample origin– destination (O-D) survey data for all of the tolled facilities operated by the New York Metropolitan Transit Authority (MTA) (2006) and by the Port Authority of New York and New Jersey (PANYNJ) (2007). Both include weekday and weekend travel by auto drivers and provide observed characteristics of tolled-crossing users and their trips by E-ZPass, cash, and dis- counted classes. Both sets of surveys provide the basic seg- mented traveler, household, and trip data that are critical to modeling. These survey data could support the modeling of transponder acquisition, including the use of data obtained from cash users regarding why they did not use E-ZPass. For the MTA and PANYNJ tolled crossings, detailed traffic count data by toll class and 15-minute intervals are available to support the O-D analysis for these facilities. Seattle Household Activity Survey The Seattle Household Activity Survey was carried out for the Puget Sound Regional Council (PSRC) and Washington State Department of Transportation in 2006 (Cambridge Systematics, Inc., Mark Bradley Research and Consulting Review and Selection of Data Sources

33 et al. 2007). The survey was based on a 2-day travel and activity diary and was carried out on nearly 4,000 house- holds. The households were selected on a geographically stratified basis to enrich the sample in regions with high transit accessibility and opportunities for non motorized travel. Intercept samples for ferry riders and park-and-ride users were also included. This survey data set has been used as the basis for activity-based model development at PSRC. For the C04 project, the Seattle RP data were analyzed at both the tour and trip levels to create models of TOD choice and mode choice. It is important to note that there were no tolled facilities (apart from one bridge) in the Puget Sound region at the time of the survey, so the data is not informative for RP analysis of congestion pricing. The tour file has records for 26,950 tours, with data for over 300 variables, including data on highway travel times in both tour directions for 17 periods during the day. The trip file has records for 73,963 trips, with the same highway LOS variables that are included for tours (but only for the trip direction). Generation of Network Level of Service and reliability Measures Standard Skimming Procedures and Network Level of Service Variables The standard components of the highway utilities terms can be derived from the standard network skimming procedures found in commercial travel demand forecasting software that generates O-D matrices of travel time LOS and costs. Based on the results of a static and general or user equilibrium assign- ment procedure, for each O-D pair these methods generate a single value for each of the standard set of fixed highway time and cost measures, which are interpreted as expected or aver- age values, and typically include the following: • Total travel time; • Vehicle operating cost (or distance-based function); and • Toll or other road user costs. As discussed in the context of the RP model estimation work reported in Chapter 3, some additional measures can be generated with these standard methods that can further spec- ify highway utilities with respect to evaluation of congestion and pricing conditions, including travel times segmented by LOS, speeds, or roadway type (or some combination of these factors). Like the more conventional measures, these augmented measures remain a single expected fixed value; they do not directly capture any measure of the variability of highway travel conditions that is seen to affect travel choices and are associated with reliability in particular. Method for Generating Travel Time Distributions The fact that a distribution of travel times is not generated by standard highway assignment software has two important implications for the research done in the C04 project: • First, it means that measures of network travel time reli- ability taken from such distributions are not available to be used in conjunction with RP survey data to estimate mod- els with measures of travel time reliability incorporated in the utilities for highway travel choices. As a result, a vast majority of the models with travel time reliability measures have been estimated in SP settings, in which travel time dis- tributions are predefined as part of the hypothetical choice set respondents consider; and • Second, without the ability to simulate travel time reliabil- ity from standard network assignment procedures, it is not possible to generate these inputs as part of the application of models that include estimated sensitivities to travel time reliability for policy or project forecasting. Consequently, for the C04 project, a special set of methods was developed for synthesizing a distribution of consistent path-dependent O-D travel times from the distributions of modeled link traffic volumes. This method allows for creating so-called reliability skims that have been used with both the New York and the Seattle-area RP survey data for the estimation of models with a travel time reliability component. Documentation of the data and methods developed to cre- ate these LOS distribution skims needed for the analysis of reliability with RP survey data is provided in Appendix A. Stated preference Data Stated Preference Extension of Seattle Household Survey For the 2006 PSRC Seattle Household Activity Survey described in this chapter, respondents who had reported trips in rel- evant transit and highway corridors were selected to partici- pate in a follow-on SP survey. Customized SP scenarios were created based on the reported trip and mailed to respon- dents. The SP survey was designed by Cambridge Systemat- ics and Mark Bradley Research and Consulting. There were two SP experiments: one was tied to mode choice (bus, rail, and auto), and the other related to TOD tolling on major highways. For the C04 study, the focus was on the latter tolling experiment.

34 A sample SP scenario is shown in Figure 2.1. Each scenario included four choice alternatives: • Travel on a free route outside peak periods; • Travel on a tolled route outside peak periods; • Travel on a free route during peak periods; and • Travel on a tolled route during peak periods. Thus, these data allow estimation of a joint model of route type (tolled versus nontolled) and departure time (peak versus off peak). In addition to the toll and travel time variables, which are included in all SP experiments of this type, this experiment had two additional variables of interest: • Distance Traveled. Because the free route may be an entirely different road than the tolled route, there may be a signifi- cant difference in terms of distance. In typical RP data, dis- tance is so highly correlated with travel time that it is not feasible to estimate separate time and distance coefficients. This SP data allow the team to estimate such an effect; and • Reliability of Travel Time. Here, a significant extra delay was defined as “more than 15 minutes late” (beyond the usual travel time), and the scenarios were varied in terms of how often such a delay occurs, allowing the team to estimate the effect of the frequency of delay. San Francisco Cordon Pricing Stated Preference Survey The San Francisco County Transportation Authority has recently considered the possibility of implementing cordon pricing around specific areas of downtown San Francisco, California. With Federal Highway Administration (FHWA) funding, an SP survey was carried out in 2007 to aid in model- ing the effects of such a policy and set effective levels of cordon charge to influence traffic levels at different times of the day. Auto travelers to downtown were intercepted and participated in a web-based SP interview. The experiment was designed by Mark Bradley and Resource Systems Group (RSG), and the survey was carried out by RSG. An example choice screen from the survey is shown in Figure 2.2. Each scenario includes four choice alternatives: • Travel by auto and pay the cordon charge before the peak period; • Travel by auto and pay the cordon charge during the peak period; • Travel by auto and pay the cordon charge after the peak period; and • Travel by public transit. In contrast to the previous SP example from Seattle, this experiment did not include a nontolled auto alternative, Figure 2.1. Sample SP scenario.

35 because in the context of cordon pricing, that would mean not traveling to downtown San Francisco at all. (Additional survey questions about that possibility were asked, but they were not analyzed as part of the C04 project.) However, a transit option was included, both because transit to down- town San Francisco is a viable alternative and because part of the stated reason for cordon pricing would be to provide funding to maintain and improve transit services. Thus, the data from this study are suitable for estimating joint models of departure time choice and mode choice. For the auto alternatives, the variables used for this study were similar to those used for the Seattle SP described in the previous section, except that • The definition of the peak period used for a given respon- dent was customized based on their actual departure time, and the duration and timing of the peak pricing period was varied across respondents, allowing a more detailed analysis of departure-time shifting behavior; • For a given respondent, the effect of reliability was measured by fixing the frequency of delay and varying the length of the delay across the alternatives. This is the opposite of how it was presented in the Seattle SP survey. Frequency was varied randomly across respondents, with “1 out of 10 trips” used for half of the sample and “1 out of 5 trips” used for the other half; and • All three auto alternatives involved using the same route, so there was no difference in distance. Los Angeles County Managed-Lane Stated Preference Survey The County of Los Angeles, California, is considering intro- ducing new managed lanes (express and high-occupancy toll [HOT] lanes) in specific freeway corridors. As part of the pre- paratory research, an SP experiment was carried out in 2009. Residents of relevant areas were contacted by telephone and recruited if they had made a recent trip by auto using one of the relevant freeways. They were asked for key details of their trip, mailed an SP questionnaire with customized choice sce- narios, and then contacted again by telephone to retrieve the responses. The SP experiment was designed by Mark Bradley and PB Americas, and the survey was conducted by Corey, Canapary and Galanis. An example choice scenario is shown in Figure 2.3. If a per- son indicated he or she would travel in the off-peak period, a Figure 2.2. Sample choice screen from San Francisco SP survey.

36 follow-on question was included to ask if the person would travel before or after the peak. So, there were effectively seven choice alternatives: • Use the express lane during the peak period; • Use the express lane before the peak period; • Use the express lane after the peak period; • Use the existing free lanes during the peak period; • Use the existing free lanes before the peak period; • Use the existing free lanes after the peak period; and • Use a new bus service via the express lane (in any period). With these alternatives, it is possible to estimate a joint model along three separate dimensions—route-type choice (tolled versus nontolled), departure time choice, and mode choice—thus combining the scope of the two preceding examples. As in the San Francisco SP example, the definition of the peak pricing period was varied systematically across respondents, and customized somewhat to be relevant for each respondent’s actual time of travel, allowing detailed analysis of departure-time shifting. In contrast to the two preceding examples, no explicit travel time reliability variable was included in the scenarios. This decision was made inten- tionally, because the forecasting framework in which the mod- els will be applied does not include measures of reliability, and including such a variable could influence the estimate for the main travel time coefficient. Experimental Data Seattle Traffic Choices Study The Seattle region currently does not have tolled or priced facilities that would provide much useful data for RP mod- eling of congestion pricing effects. It does, however, have one unique data set from a recent experiment (Puget Sound Regional Council 2008) that served as one of the principal data sets used for the C04 model estimation. In this experi- ment, recruited households were given a real monetary budget, and money was deducted from the account every time they used certain roads at certain times of the day and week. Respondents were given a pricing schedule and map, as well as an in-vehicle meter that showed the price when- ever they were being charged (Puget Sound Regional Coun- cil 2008). Almost 300 households participated in the Traffic Choices Study for a period of more than 1 year. During that period, GPS data were collected for all trips made in the respondents’ vehicles, covering the time span before, during, and after a period when experimental distance-based pricing was administered (for those respondents only). At the beginning of the study each household was given a monetary budget and a schematic pricing chart, with the per mile charge vary- ing by facility type, day of week, and TOD (see the pricing chart in Figure 2.4). Every time one of the household’s vehicles Figure 2.3. Sample choice scenario from the Los Angeles County SP survey.

37 drove on one of the priced highway links, the distance was recorded by the GPS unit, and the user charge was displayed on a taxi meter–like device in the vehicle and deducted from the household’s remaining budget. The household could keep any budget remaining at the end of the pricing period. Thus, driv- ing on the priced links during the experiment cost them real money that they would otherwise get to keep. In theory, the Traffic Choices data can be used to esti- mate disaggregate, trip-level joint models of route-type choice and departure time choice, as the price varied across link types and times of day and week. In practice, because the data are in GPS format and the study was not designed for this particular type of analysis, the team found it to be extremely challenging to use these data for choice model- ing. Because the data are so potentially informative, how- ever, and because GPS traces will be an increasingly common source of data for travel demand models, it is worthwhile to report the team’s experiences and findings in analyzing these data. The data set includes GPS traces for almost 1,000,000 auto trips in the region, and many of those are for the same indi- viduals making trips between the same locations at the same TOD over an extended period of many months. This means that these data could be used to obtain both average speeds and travel time variability for many highway links and O-D pairs in the region. While the geographic coverage of this information is not sufficient to use in a general travel-demand model estimation for the region, it nevertheless provides a useful comparison to generalize to other network-based mea- sures of travel time variability and reliability used in this project. Figure 2.4. Schematic pricing chart in the Seattle Traffic Choices Study.

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TRB’s second Strategic Highway Research Program (SHRP 2) Report S2-C04-RW-1: Improving Our Understanding of How Highway Congestion and Pricing Affect Travel Demand includes mathematical descriptions of the full range of highway user behavioral responses to congestion, travel time reliability, and pricing. The descriptions included in the report were achieved by mining existing data sets. The report estimates a series of nine utility equations, progressively adding variables of interest.

The report explores the effect on demand and route choice of demographic characteristics, car occupancy, value of travel time, value of travel time reliability, situational variability, and an observed toll aversion bias.

An unabridged, unedited version of Chapter 3: Demand Model Specifications and Estimation Results is available electronically.

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