Assessing Highway Tolling and Pricing Options and Impacts: Volume 2: Travel Demand Forecasting Tools (2012)

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20 3.1 Overview of Survey Methods to Support Pricing Studies One of the major factors affecting model accuracy relates to the quality of the data used in model estimation, calibra- tion, and validation. Tremendous progress has been made in recent years with respect to data collection technology and new types of surveys, to the point that it is cost-effective to consider such data collection efforts. This chapter discusses the advantages of complementing traditional data sources (home interview surveys and annual average daily traffic counts) with sources that better target potential toll custom- ers. This includes GPS-assisted surveys, processing infor- mation available from electronic toll collection systems, combined revealed and state preference surveys, and traffic choices experiments (like the one implemented in Seattle). Techniques that significantly improve the quality and com- prehensiveness of the data would also improve the accuracy of the travel model. The following major types of surveys are applied to sup- port pricing studies (and models developed for these studies): • Travel Pattern Surveys (“Revealed Preferences”) including: – Household-Based Travel/Activity Surveys – Origin-Destination Surveys on specific facilities and existing toll roads • Stated Preference Surveys that vary significantly across the following dimensions: – Choice Dimensions and Scenario Design – Trip Attributes Relevant for Pricing Studies – Choice Context – Instrument Design – Sampling • Special Survey Types including: – Surveys of Commercial Vehicles – Behavioral Experiments and Follow-up Surveys – Attitudinal / Public Opinion Surveys A comprehensive Household Travel Survey which is gener- ally needed to develop a regional transportation model can also serve as the source for VOT and other model param- eter estimates relevant to modeling road pricing. There is, however, a growing recognition that for pricing analysis the household survey data has to be supported by complemen- tary project-specific revealed preference (RP) and/or stated preference (SP) surveys. This is especially crucial for start-up projects in regions with no prior experience with highway pricing where the RP survey cannot provide direct informa- tion about responses to unobserved choices and SP surveys are typically designed to address willingness-to-pay factors relevant for road pricing (value of time savings, value of reli- ability). Survey data collection can also support other model development data needs, including HOV/HOT lane usage and payment media choice. GPS-based supplements are included with some household surveys and these provide detailed route information for all recorded trips. Either vehicle or person- based GPS data collection can be used but vehicle-based GPS data collection is generally more useful for collecting route information, assuming that tracking routes for transit and pedestrian/bicycle alternatives is not necessary. Intercept surveys that collect information about the ori- gins and destinations (OD) and other details have been widely used to determine the characteristics of trips that are observed at selected locations (Hagen et al. 2006). These types of surveys are particularly useful for characterizing the trips that currently utilize specific corridors that are, or might be, served by a toll facility and the trips that cross into or out from a cordon that might be subjected to area pricing. This type of focused information is especially useful in estimating the numbers and types of trips that might be affected by the toll facility or area pricing. Although regional travel forecasting models can also be used to synthetically provide this infor- mation, these models are typically not sufficiently calibrated to estimate these details as accurately as can be done with an OD survey. Also, as OD surveys have shown, ETC registra- C h a p t e r 3 Survey Methods to Support Pricing Studies

21 tion lists can allow access to the current toll facility users. This greatly facilitates sampling strategy, questionnaire distribu- tion, and post-survey development of expansion factors. There are several objective limitations associated with RP surveys for modeling pricing effects: • First and foremost, they are not applicable for model estimation/calibration in new corridors located in regions where there are no current toll facilities. • Another associated problem is that with the survey of exist- ing toll facility users, a very specific choice-based sample is created since it can be difficult to define and access non- toll users. • It is difficult to collect data associated with time-of-day choice since generally only a single trip is observed and surveyed; otherwise the OD survey would need to be extended into a Household/Person Interview Survey. • It is also difficult to support data types that are necessary for measurement of travel time reliability and estimation of its impact on travelers’ choices. • RP surveys are also not extremely helpful for understand- ing and modeling mid-term choice, such as transponder acquisition. For more than 20 years, SP surveys have been used to esti- mate values of travel time and other parameters related to the effects of tolls and road pricing [see Adler and Schaevitz (1989)]. SP surveys include a set of hypothetical scenarios in which conditions (e.g., travel times, tolls) are varied and respondents are asked to indicate what they would most likely choose under those specified conditions. The conditions are varied according to an experimental plan that optimizes the information about the respondents’ preferences that each scenario provides. SP surveys are especially useful in applications where an alternative such as a toll facility does not currently exist but is being planned for the future. In those types of applications, RP surveys are not useful for estimating price effects because road prices, which are variables of interest, do not vary across trips within the region. While other cost elements such as operating costs do vary across trips, those variations are highly correlated with trip lengths and travel times, and thus generally do not provide reliable indications of the effects of price on travel choices. With respect to choice dimensions, the SP surveys con- ducted to support road pricing projects have most often focused on the choice between tolled and toll-free routes. For conventional toll facility studies, these surveys would typi- cally present two alternatives: a toll-free route with a given travel time and an alternative tolled route with a lower travel time and a toll at some level. However, many road pricing projects involve more complex effects beyond simply influ- encing route choice. Some projects, such as HOT lanes, affect occupancy and mode and so the stated preference scenarios would include other modes and occupancy levels as avail- able choice alternatives. For projects that have time-varying prices, different travel periods should be included among the stated preference alternatives. For area pricing projects, the scenarios could allow alternative destinations. In some spe- cial cases, effects on trip frequency may also be included in the stated preference experiments. Travel times and toll prices are the primary attributes in most road pricing stated preference experiments. The trade- offs between travel time savings and extra cost associated with tolls, are expressed in VOT. However, there are other attributes that may also be significant in travelers’ choices in the presence of road pricing. Some of the other attributes or features that have been tested in stated preference experi- ments for road pricing projects include: • Travel time components—time in free flow conditions and time in congested traffic, • Travel time reliability, • Occupancy-based toll levels, • FAIR (Fast AND Intertwined Regular) lanes policy, • Commercial vehicle restrictions, • ETC discounts, • Travel time variability, • Driving distance along the route, and • Non-toll “running” costs. In an SP survey, it is extremely important to set a realistic choice context. A common approach is to ask respondents if they have made a recent trip in the relevant corridor, and, if so, to ask for details on the most recent trip and use the infor- mation to customize the SP choice context. The use of the most recent trip rather than the most typical one is meant to avoid bias and replicate a random sample, just as household survey respondents are asked to complete a diary for a spe- cific day and not necessarily a typical day. A design issue that commonly arises is the limit on how long in the past the most recent trip can be to qualify for the survey. A typical strategy is to set the limit at 1 or 2 weeks prior to the interview, while a retrospective limit of longer than 1 month is rarely used in practice. SP surveys have been conducted using several different types of instruments. One important challenge is that mul- tiple SP experiments are needed from each respondent, gen- erally involving a series of trade-offs among several variables that vary across two or more travel alternatives. It can be dif- ficult for respondents to process all of this information unless it is presented visually and, for this reason, telephone-based instruments are rarely used. However, hybrid instruments can be used where trip context information is collected over

22 the phone and the stated preference experiments are pro- vided separately by mail or over the web. In addition, simpli- fied experiments can be designed that are more amenable to phone-based administration. Sampling for stated preference surveys can also be con- ducted in several ways. For facility-based studies, some type of intercept sampling is often the only viable alternative. This can be because the population using the facility or corridor is widely dispersed geographically and may, for example, include significant numbers of trips made by individuals who live well outside the region where the facility is located. Inter- cept sampling can be conducted using the methods described earlier for OD surveys, but it can also be accomplished using intercepts at activity centers in the corridor of interest. For area pricing or cordon pricing, it may be most efficient to intercept people within the potential priced area. For study- ing corridor-specific projects, it is often effective to use Ran- dom Digit Dialing (RDD) or address-based sampling within the residential areas that would be served by the project. For broader regional studies, the options are wider and include more standard phone, mail or web/email recruiting. SP sur- veys have also been administered along with conventional household travel/activity surveys, usually as an add-on to some fraction of those surveys. Recent advances in SP survey design and technology have made this tool significantly more attractive and practical, particularly in the following respects: • Computer-based SP surveys customize choice experiments around specific contexts (choice of toll road/lanes versus non-toll road/lanes, choice between road and transit, switch- ing to other time-of-day periods in presence of congestion pricing, etc). • The SP framework is extremely convenient for multiple/ repeated experiments with the same person and can be effectively employed for screening inherent randomness in travelers’ preferences that can be captured through estima- tion of probabilistic VOT distributions with models like mixed (random coefficients) logit. • The SP framework is convenient for estimation of Value of Reliability along with VOT and other possible impacts. • Additionally, SP allows for more efficient experimental design with multiple alternatives, while the RP sample structure is bound to the observed frequencies of different alternatives. • SP survey can be designed to include transponder acquisi- tion in the model’s choice hierarchy. • SP survey is an effective tool in capturing different price perceptions, for example ETC users versus cash users. SP surveys do have their own limitations. Incorporating all relevant choices leads to complex designs which may confuse respondents. Thus, SP surveys are only effective as a focused tool. SP surveys also have inherent strategic biases. For these reasons, the most promising direction for model estimation is to use a combination of SP and RP surveys that allows for elimination of strategic biases by statistical scaling procedures. A more detailed analysis of travel survey techniques for road pricing with numerous examples can be found in Appendix A (Section A.2). 3.2 Summary and Proposed Practice Guidelines The implemented extensive analysis of specific pricing RP and SP surveys used to support existing applied models has revealed the following general patterns, with the follow- ing conclusions offered and possible directions for further research identified: • At the stage of exploratory/preliminary analysis, data collec- tion is often limited to secondary data (traffic counts, land- use changes). The demand functions and utility expressions for choice models, as well as the coefficient values themselves, are frequently borrowed from other areas and adjusted using household income and other socio-economic data. • If a project is determined to be feasible, primary data col- lection is conducted. Investment grade studies typically include extensive data collection, with special OD surveys conducted for most major toll projects. • In some cases (where toll facilities already exist) OD surveys can be used for RP modeling. In many cases, where a facil- ity is in a “new” corridor without current tolling, models are “borrowed” if the extensions of local model cannot be supported with the conduct of new RP and/or SP surveys. Currently there is a large and growing opportunity for RP surveys in a wide variety of the existing toll corridors due to a number of reasons: • Pricing analysis can strongly benefit from a systematic statis- tical analysis of observed VOT and other behavioral param- eters would be possible if the data could be made available. This is one of the major directions of the SHRP 2 C04 project closely coordinated with the current NCHRP 8-57 project. • In some corridors, along with the demand pattern, accu- rate travel time estimates can be provided. Otherwise travel times and travel time savings for statistical analysis are calculated in the network simulation model, although it should be taken into account that travel time estimates on congested facilities from network simulation models are inaccurate approximations. • Also, as the experience of recent OD surveys has shown, ETC registration lists can allow access to the current toll

23 facility users. This greatly facilitates sampling strategy, questionnaire distribution, and post-survey development of expansion factors. There are several objective limitations associated with RP surveys: • They are not applicable for model estimation/calibration in new corridors located in regions where there are no cur- rent toll facilities. • With the survey of existing toll facility users, a very specific choice-based sample is created since it can be difficult to define and access non-toll users. • It is difficult to collect data associated with time-of-day choice since generally only a single trip is observed and sur- veyed; otherwise the OD survey would need to be extended into a Household/Person Interview Survey. • It is also difficult to support data that is necessary for mea- surement of travel time reliability and estimation of its impact on traveler’s choices. • RP surveys are also not extremely helpful for understand- ing and modeling long-term choice, such as transponder acquisition. Recent advances in SP survey design and technology have made this tool significantly more attractive and practical, particularly in the following respects: • Computer-based SP surveys customize choice experiments around specific contexts (choice of toll road/lanes versus non-toll road/lanes, choice between road and transit, switch- ing to other time-of-day periods in presence of congestion pricing, etc). • The SP framework is extremely convenient for multiple/ repeated experiments with the same person and can be effectively employed for screening inherent randomness in travelers’ preferences that can be captured through estima- tion of probabilistic VOT distributions with models like mixed (random coefficients) logit. • The SP framework is convenient for estimation of Value of Reliability along with VOT and other possible impacts. • Additionally, SP allows for more efficient experimental design with multiple alternatives, while the RP sample structure is bound to the observed frequencies of different alternatives. • SP survey can be designed to include transponder acquisi- tion in the model’s choice hierarchy. • SP survey is an effective tool in capturing different price perceptions, for example ETC users versus cash users. • SP surveys have their own limitations. Incorporating all relevant choices leads to complex designs that may confuse respondents. Thus, SP survey is only effective as a focused tool. SP surveys also have inherent strategic biases. For these reasons, the most promising direction for model estimation is to use a combination and SP and RP surveys that allows for elimination of strategic biases by statistical scaling procedures. These survey and data collection methods constitute a suite of options that can be used to support the analysis of road pricing programs. The decision about which of these methods to employ depends on several factors, including the stage of decision making that the analysis and modeling must support, the types of data and models available for use and, of course, the schedule and budget for the work. Table 4 below provides some general guidelines for the types of data that might be used to support the different stages of project development. In this table, X represents items that are gener- ally required in some form to support the stage and O repre- sents items that may be appropriate depending on the project importance and complexity. Project Stage Survey type Household Interview Origin- Destination Stated Preference Opinion Highway Speed Traffic Counts Exploratory screening X X Preliminary feasibility X O O O X Feasibility evaluation X X O O X X Investment Grade X X X O X X Table 4. Highway pricing survey and data collection needs.