Route choice sets for very high-resolution data

With the increasing use of GPS in transport surveys, analysts can not only choose from numerous new ways to model travel behaviour but also face several new challenges. For instance, information about chosen routes is now available with a high level of spatial and temporal accuracy. However, advanced post-processing is necessary to make this information usable for route choice modelling. Out of many related issues, this article 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 made by 2434 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 chosen routes. However, it substantially increases the requirements for the choice set generation algorithm in regard to performance as well as choice set composition. This article presents a new route set generation based on shortest path search with link elimination. The proposed procedure combines a Breadth First Search with a topologically equivalent network reduction and ensures 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 with those of a stochastic choice set generation algorithm.