Several recent pilot studies combined Global Positioning System (GPS) technology with travel survey data collection to evaluate opportunities for improving the quantity and accuracy of travel data. These studies used GPS to supplement traditional data elements collected in paper or electronic travel diaries. Although many traditional trip elements can be obtained from the GPS data, trip purpose has remained an important element, requiring the use of a diary to continue. Presented are the results of a proof-of-concept study conducted at the Georgia Institute of Technology that examined the feasibility of using GPS data loggers to completely replace, rather than supplement, traditional travel diaries. In this approach, all GPS data collected must be processed so that all essential trip data elements, including trip purpose, are derived. If this processing is done correctly and quickly, then the computer-assisted telephone interview retrieval call could be shortened significantly, reducing both respondent burden and telephone interview times. The study used GPS data loggers to collect travel data in personal vehicles. The GPS data were then processed within a geographic information system (GIS) to derive most of the traditional travel diary elements. These derived data were compared with data recorded on paper diaries by the survey participants and were found to match or exceed the reporting quality of the participants. Most important, this study demonstrated that it is feasible to derive trip purpose from the GPS data by using a spatially accurate and comprehensive GIS.

Trip underreporting has long been a problem in household travel surveys because of the self-reporting nature of traditional survey methods. Memory decay, failure to understand or to follow survey instructions, unwillingness to report full details of travel, and simple carelessness have all contributed to the incomplete collection of travel data in self-reporting surveys. Because household trip survey data are the primary input into trip generation models, it has a potentially serious impact on transportation model outputs, such as vehicle miles of travel (VMT) and travel time. Global Positioning System (GPS) technology has been used as a supplement in the collection of personal travel data. Previous studies confirmed the feasibility of applying GPS technology to improve both the accuracy and the completeness of travel data. An analysis of the impact of trip underreporting on modeled VMT and travel times is presented. This analysis compared VMT and travel time estimates with GPS-measured data. These VMT and travel time estimates were derived by the trip assignment module of each region's travel demand model by using the trips reported in computer-assisted telephone inter views. This analysis used a subset of data from the California Statewide Household Travel Survey GPS Study and was made possible through the cooperation of the metropolitan planning organizations of the three study areas (Alameda, Sacramento, and San Diego, California).

Poor health outcomes from insufficient physical activity (PA) are a persistent public health issue. Public transit is often promoted for positive influence on PA. Although there is cross-sectional evidence that transit users have higher PA levels, this may be coincidental or shifted from activities such as recreational walking. We use a quasi-experimental design to test if light rail transit (LRT) generated new PA in a neighborhood of Salt Lake City, Utah, USA. Participants (n=536) wore Global Positioning System (GPS) receivers and accelerometers before (2012) and after (2013) LRT construction. We test within-person differences in individuals’ PA time based on changes in transit usage pre- versus post-intervention. We map transit-related PA to detect spatial clustering of PA around the new transit stops. We analyze within-person differences in PA time based on daily transit use and estimate the effect of daily transit use on PA time controlling for socio-demographic variables. Results suggest that transit use directly generates new PA that is not shifted from other PA. This supports the public health benefits from new high quality public transit such as LRT.

The recent Swedish Intelligent Speed Adaptation (ISA) study included a component that involved the installation of units based on the Global Positioning System (GPS) in hundreds of cars in three Swedish cities, Borlänge, Lund, and Lidköping; these vehicles were observed for up to 2 years. In Borlänge, the speed and location data of each vehicle were transmitted at regular intervals to a central server and stored for later analysis. This data set contains a wealth of travel behavior information that had not been available before. However, a data set of this magnitude introduces a major need for automated processes that can glean travel behavior details from the trip summary and collected GPS point files. A summary is presented of characteristics of and issues with the Borlänge GPS data set, which included 186 personal vehicles with at least 30 days of travel data and corresponding household sociodemographic data. (These 186 vehicles recorded 49,667 vehicle days of travel and 240,435 trips inside the study area.) Then, automated methodologies are presented for imputing trip purpose for these trips once the trip destinations are identified, as well as for correcting the GPS traces and identifying missing trip ends within these trips. Results of these automated processes for a subset of the ISA study vehicles are included.

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