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Measuring Day-to-Day Variability in
Travel Behavior Using GPS Data


Summary and Implications

In this study, day-to-day variability in travel behavior is measured using GPS data collected from a sample of 100 households in the Lexington, Kentucky area. One vehicle in each of the 100 households was fitted with a GPS device and a personal digital assistant (PDA) device in order to track and record movements of the vehicle over a period of several days. Most households provided at least three days of complete and usable information, thus providing a valuable data set for measuring day-to-day variability and for examining the potential of adopting GPS technologies in larger scale travel data collection efforts. The analysis in this study focused on trips reported by primary drivers and did not include secondary and tertiary drivers who may have also used the GPS-fitted vehicle for trip making purposes.

The first analysis performed for this study focused on comparing aggregate sample-wide averages across the days for which travel information was reported. The comparison was done across seven days without regard for the day of the week, across five weekdays, and across the two weekend days. In general, it was found that sample-wide averages show considerable stability across multiple days. However, when one controls for day of week effects, some differences in overall trip frequency are observed towards the end of the week (Thursday and Friday). This finding is consistent with that found by Zhou and Golledge [2000]. As expected, some differences in trip making patterns were found between weekdays and weekend days with regard to trip frequencies and first time of departure from home.

Interestingly, travel distances and total travel time were quite similar between weekdays and weekend days. In general, it was found that the GPS data set yielded trip frequencies slightly higher than the typical average frequencies reported in NPTS and other traditional travel surveys. This is presumably because of the selective nature of the sample which includes people who drive at least three days per week and are 18 years or older. In addition, the GPS travel data collection method is better able to capture short and infrequent trips that occur within trip chains [Yalamanchili, et. al., 1999].

As tracking aggregate sample-wide averages from one day to the next may mask disaggregate within-person variability that is present in a data set, further analysis was performed. First, an analysis was done to assess the degree of repetition of behavior in the sample. The sample was divided into groups based on the total number of days for which they provided complete usable travel information. Then, the percents of each sample group that repeated behavior on all reported days, all but one reported days, and all but two reported days were determined. In general, it was found that the percentage of individuals who repeated their behavior on all days is extremely small regardless of the travel characteristic being examined. If one were to control for day-of-week, then the degree of repetition of behavior is found to increase, but only modestly. Consistent with research reported in the literature, this analysis showed that there is considerable variability in behavior across multiple days.

In an effort to quantify day-to-day variability in travel behavior and measure the extent of intra-personal (within-person) variability, the methodology adopted by Pas [1987] and Pas and Sundar [1995] was applied to the Lexington GPS data set. In this approach, the total variability in the data set consists of two components - inter-personal variability and intra-personal variability. By representing total variability by the total sum of squares, inter-personal variability by the between-person sum of squares, and intra-personal variability by within-person sum of squares, the percent of total variability that may be attributed to intra-personal variability may be determined. This analysis was done for several different travel characteristics including trip frequencies, travel times, travel distances, and selected departure/arrival times.

The analysis showed that the extent of intra-personal variability measured in a data set is sensitive to two dimensions. First, it is sensitive to the day of the week effect. If one controls for the day of the week when analyzing variability across multiple days, then the degree of intra-personal variability measured is lower. Second, it is sensitive to the period or duration for which travel is observed. The longer the period of observation, the greater the degree of intra-personal variability. Both of these findings are consistent with the literature, thus showing that GPS-based travel data sets are capable of offering plausible and reasonable inferences on travel characteristics.

In comparing the estimates of intra-personal day-to-day variability in travel offered by the GPS-based data set with those reported in the literature by Pas [1987] and Pas and Sundar [1995], it is found that the estimates obtained from the GPS data set in this study are consistently greater than those reported in the literature by nearly 10 percentage points. Further research is needed to clearly identify the sources of this difference; however, several hypotheses may be put forth here. First, there may be socio-economic differences between the samples under consideration that may be partially contributing to this difference. The GPS-based data set used in this study is a small sample data set, includes only those trips undertaken by the primary driver using the GPS-fitted vehicle, and includes a self-selected sample of households that has larger household sizes, vehicle ownership, income, and education levels. The extent to which socio-economic differences and sample composition contribute to differences in measures of intra-personal variability merit further investigation.

A second major hypothesis, and more noteworthy in the context of the growing interest in GPS-based data collection methods, is that GPS technologies are able to better capture variability in travel behavior across multiple days. There are two potential inter-related reasons for this. First, as demonstrated by Yalamanchili, et. al. [1999], the GPS-based data set captures (to a greater extent) the short and infrequent trips that may not be obtained in a traditional travel diary survey. As these short and infrequent trips are quite different from one day to the next, they contribute to a greater degree of intra-personal variability than if they were omitted from the analysis. Second, in a multiday travel diary survey (especially those exceeding two days), there is likely to be diary fatigue [Pendyala and Pas, 2000]. Even if a respondent provides very detailed information (including that on short and infrequent trips) on one or two days, it is likely that the quality of the information will deteriorate on subsequent days. Usually diary fatigue manifests itself in the form of unreported trips (usually the short and infrequent trips go unreported) and missing data items. Then, it is likely that a multiday GPS travel data set that includes detailed information on all trips across all days will provide a greater (and potentially more accurate) measure of intra-personal variability than a multiday travel diary data set where some infrequent trips go unreported in latter days of the survey.

Overall, it appears that GPS based travel data sets are able to:

More importantly, GPS-based data collection methods accomplish the above without placing undue or additional burden on the respondent. Battelle [1997] reports that it took 75 percent of the respondents less than one minute to enter the trip information into the PDA device. This is in stark contrast to the 10 minute reporting durations for paper diary surveys and 20-25 minute durations over the phone. Also, the presence of the GPS receiver greatly reduces the number of questions or attributes that are required to be entered manually by the respondent. For example, in the GPS based survey, start time, end time, origin and destination locations, distance, travel time, and route information are automatically logged. Given these potential advantages, it is no surprise that GPS-based data collection methods are gaining increasing popularity for use in travel surveys. Several major ongoing travel surveys include GPS-based data collection components.

Within the scope of this study, the full potential of GPS based travel data sets was not exploited. Detailed route choice, spatial location, and travel itinerary data present in the data set was not used in the analysis of this study. Further research should examine day-to-day variability in route choice, spatial location, and action space; such an analysis would be unique as it is very difficult to perform such analyses using traditional travel diary survey data sets.

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