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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.

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The Georgia Institute of Technology is evaluating the feasibility and effectiveness of mileage-based pricing programs as transportation control measures. Incentives were provided to study participants who change driving behavior in response to cent per mile pricing (fixed pricing and pricing as a function of congestion level). In-vehicle Global Positioning System (GPS) devices were used to estimate distance traveled and driver behavior (e.g., speed and acceleration profiles). The accuracy of estimated mileage accrual speeds by road classification, and acceleration rates used in pricing algorithms, is paramount. Various data-smoothing techniques were applied to the instrumented vehicle GPS speed data, and performance of the algorithms was evaluated in minimizing the impact of GPS random error on speed, acceleration, and distance estimates. The conventional discrete Kalman filter algorithm was modified to enhance its ability to control GPS random errors. Each smoothing method produces different second-by-second speed and acceleration profiles ( t-test and χ2 tests) except for the Kalman filters. The techniques provided different travel distance estimates, but the modified Kalman filter was the most accurate compared with distance estimates from the onboard vehicle speed sensor monitor. The modified Kalman filter is the recommended technique for smoothing GPS data for use in pricing studies. Additional smoothing methods will be evaluated as they are identified.

This paper examines morning commute route choices of 182 drivers, with the use of disaggregated Global Positioning System–based vehicle activity data collected during a 10-day period. This paper attempts to describe how these commuters tend to behave in the real world. A binary logit model of morning commuters’ choice between a single commute route and multiple routes was established on the basis of evidence of drivers’ varying valuations of a number of route and trip characteristics as well as commuters’ sociodemographic characteristics. The research results of this paper indicate a strong relationship between the morning commute route choice decision (single versus multiple routes) and commuters’ work schedule flexibility, sociodemographic characteristics, and commute route attributes.

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