I am pleased to report that a new FHWA-supported study: “Multiday GPS Travel Behavior Data for Travel Analysis” (PDF) is out. Our contribution is Chapter 4.0 An Empirical Study of the Deviation between Actual and Shortest-Travel-Time Paths. The report was coordinated by RSG, and the overview is below:

Introduction to Report

By Mark Bradley (RSG)

The use of GPS devices to collect trip-specific data as part of household travel surveys has increased steadily in recent years, and will likely become the main mode of travel survey data collection in the future as smartphone-based platforms for collecting travel data come into use. Compared to diary-based methods, the advantages of GPS data capture include the following:

• The time and location of each trip end can be captured with more precision.
• There is less potential for respondents to omit entire trips or activities from the survey.
• The data can be used to trace the route traveled for any particular trip.
• It becomes more cost-effective to capture multiple days of travel for each respondent.

These unique aspects of GPS data enable new types of behavioral analysis relative to those conducted with more traditional travel survey data. In particular, multiday data capture, in combination with more precise and complete travel data on each day, allows researchers to investigate day-to-day variability in travel behavior at the individual and household level. Such analyses can provide more insight into peoples’ travel patterns at a broader level, and guide future efforts in modeling and predicting travel behavior and designing transportation policies.

Large-sample, multiday GPS datasets from household travel surveys are still relatively limited in quantity, as is the expertise required to process point-by-point GPS trace data into trip-level data that can be used by most analysts. To address these issues, the US Department of Transportation and the National Renewable Energy Laboratory (NREL) have created the Transportation Secure Data Center (TSDC).[1] The TSDC allows researchers to access preprocessed data from almost one dozen different multiday GPS travel datasets from across the United States; it also allows researchers to analyze these data in a secure environment that ensures the protection of data privacy.

The two main objectives of this project are: 1) to provide new examples of the type of valuable research that can be done using multiday GPS travel survey data; and 2) to demonstrate that such research can be conducted in the TSDC research environment. Each of the following four chapters describes a research project that was funded and carried out as part of this project. The four research topics were originally specified by RSG, with input from FHWA, and then further refined by the authors during the course of their research.

In “The Effect of Day-to-Day Travel Time Variability on Auto Travel Choices,” Jennifer Dill, PhD, and Joseph Broach, PhD (candidate), of Portland State University address the important research topic of measuring the effect of auto network reliability on drivers’ choices. Using data from a 7-day vehicle-based GPS survey in the Atlanta region and a longer-duration vehicle-based GPS survey in the Seattle region, the authors identified several cases where respondents made multiple car trips between the same origin-destination (O-D) pairs during the survey period, and measured the actual experienced day-to-day travel time variation for those O-D pairs. The authors report several interesting analyses showing that such variability is related to trip and traveler characteristics, including trip purpose, distance, and household income.

In “Multiday Variation in Time Use and Destination Choice in the Bay Area Using the California Household Travel Survey,” Kate Deutsch-Burgner, PhD, of Data Perspectives Consulting, investigates day-to-day variation in the number, types, and level of dispersion (distance) of destinations visited during the specific days of a 3-day person-based GPS survey in the California Bay Area. Using the technique of latent class cluster analysis (LCCA), she is able to distinguish clearly different patterns of variability in terms of number of trips and type and dispersion of destinations. This analysis method shows promise for addressing the complexity of multiday travel data, and may become even more useful as future person-based (e.g., smartphone-based) GPS datasets include a greater number of travel days and a potentially wider variety of different patterns across the days.

In “Capturing Personal Modality Styles Using Multiday GPS Data-Findings from the San Francisco Bay Area,” Yanzhi “Ann” Xu, PhD, and Randall Guensler, PhD, of Trans/AQ, Inc., analyze the same multiday GPS dataset from the Bay Area that was used for the analysis described in the preceding chapter. In this analysis, however, the focus is on day-to-day variation in mode choice-research for which person-based, rather than vehicle-based, GPS data collection is clearly necessary. The authors were able to identify distinct groups of individuals in terms of whether they always used the same mode or used a variety of modes, and in terms of whether auto or alternative modes were used more often. They were also successful in relating these groupings to different person and household characteristics. The propensity to use multiple modes would benefit standard travel modeling methods, as someone who usually uses auto but also uses transit one or two days per week may be more likely to increase his or her transit use in response to service changes, as compared to someone who never uses transit at all.

Finally, in “An Empirical Study of the Deviation between Actual and Shortest-Travel-Time Paths,” Wenyun Tang, PhD (candidate), and David Levinson, PhD, of the University of Minnesota, use multiday person-based GPS data from the Minneapolis region Travel Behavior Inventory (TBI) to determine how often drivers use the shortest path for their home-to-work trip, and look at patterns in the deviation in travel time between the shortest path and the actual path. In terms of day-to-day variability, the authors were not able to identify many cases where respondents made the same direct home-to-work auto trip on multiple days. This outcome indicates that analyses that measure travel behavior across multiple days (rather than simply treating them as separate single days) will tend to require large sample sizes, particularly when the analysis focuses on a specific type of behavior (e.g., direct home-to-work auto trips).

The research presented in the following four chapters provides interesting findings in their own right, and insights into the types of research designs and methods that will be valuable in analyzing multiday GPS data as it becomes more ubiquitous and accessible in the future. The authors generally recognize that their methods could benefit from larger sample sizes, in terms of the number of respondents, and particularly in terms of the number of days per respondent. (For example, use of 7-day GPS data capture periods would allow analysis of patterns, including both weekdays and weekends.) The authors also note the critical importance of how the GPS trace data are processed into trip-level data, and the need for evolving practices and standards in GPS data processing. Finally, the authors describe the value of the TSDC in making these unique datasets available while providing a secure and productive research environment.