Route Choice Sets for Very-High-Resolution Data

Abstract

With the increasing use of global positioning systems (GPS) in transport survey analysts are facing numerous new possibilities to model transport behavior but also several new challenges. For instance, information about chosen routes is now available with a high level of spatial and temporal accuracy. However, advanced postprocessing is necessary to make this information usable for route choice modeling. Out of the many research issues, this paper focuses on the generation of choice sets for car trips extracted from GPS data. The aim is to generate choice sets for about 36,000 car trips from 2,434 persons living in and around Zurich, Switzerland, on the Swiss Navteq network, a very high-resolution network. This network resolution is essential for an accurate identification of the chosen routes. However, it substantially increases the requirements for the choice set generation algorithm regarding the performance as well as the choice set composition. This paper presents a route set generation based on shortest path search with link elimination. The proposed procedure combines a \emph{Breadth First Search} with a \emph{topologically equivalent network reduction} to ensure a high diversity between the routes as well as computational feasibility for large-scale problems like the one described above. To demonstrate the usability of the algorithm, its performance and the resulting route sets are compared to those of a stochastic choice set generation algorithm.