Abstract
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.
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