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.

The paper describes recent experience with the application of an innovative Global Positioning System (GPS)–assisted prompted recall (PR) method for a large-scale household travel survey (HTS) in Jerusalem, Israel. The survey was designed to support development of an advanced activity-based model (ABM). The requirements for an HTS to support an advanced ABM are discussed, and the corresponding decisions for survey methods are substantiated. Development of an advanced ABM requires individual records for the entire daily pattern without gaps, missing trips, overlaps, or other data inconsistencies found in a conventional HTS. A consistent record of joint activities and trips of multiple household members is essential. In addition, high levels of spatial and temporal resolution are required. The GPS-assisted PR survey has been identified as the most promising methodology for meeting these requirements. The experience of the first phase of the Jerusalem HTS in 2010 proved the feasibility of the GPS-PR method for all population sectors including specific Orthodox Jewish and Arab populations, which typically featured large household sizes. Various structural comparisons of trip and tour rates obtained during the first phase of the Jerusalem GPS-assisted HTS (3,000 households) with the non-GPS surveys previously implemented in Jerusalem and several metropolitan regions in the United States as well as comparisons between the GPS and non-GPS subsamples within the Jerusalem HTS were made. The results confirmed the ability of the GPS-PR approach to create full and consistent daily records of individual activity travel patterns and practically eliminate the underreporting issues that have plagued HTS.

Data needs for developing travel demand models have increased at the same time that household travel survey (HTS) participation rates have generally fallen over recent decades. GPS-assisted HTS methods are recognized today as the most promising direction in further enhancement of individual travel data collection. The principal advantage of the GPS-assisted survey technology is that a full stream of locations visited by the person is identified with a high level of spatial and temporal resolution, but automatic identification of trip purpose remains an issue that is difficult to solve. This paper evaluates the performance of two methods, choice modeling and decision tree analysis, that can be used to build models capable of identifying trip purpose. The developed methods assume that basic household- and person-level data, typically collected in an HTS, are available, as are supporting spatial data sets. The methods presented are then evaluated for a case study that employed data from the 2011 Atlanta Regional Commission HTS. The developed models produced encouraging results with overall accuracy greater than 70% across all purposes and around 90% for mandatory activities (i.e., work and school). The performance of the developed models was evaluated in terms of error rates by purpose category and the impact of ancillary spatial data. The paper concludes with a summary of the findings and recommendations for practitioners.