Development of a trip reconstruction tool for GPS-based personal travel surveys

To collect travel survey data for transportation models more precisely and conveniently, Global Positioning System (GPS) has been used as an assistant tool in some recent surveys. However, the current applications still involve substantial manual data processing. This calls for new approaches that derive essential travel data through automated processing of GPS data.; The purpose of this research is to develop a trip reconstruction tool, identifying used links and modes, for GPS-based personal trip surveys. The former is based on a conventional map-matching algorithm, and the latter is a rule-based algorithm using features of 4 modes (walking, bicycle, bus and passenger-car). The developed methodologies were evaluated with GPS travel data and transportation networks of downtown Toronto. Compared with records of a respondent, results of the tool show that 78.5% of all traveled links were detected, and 91.7% of modes of all trips were classified correctly.