Addressing the missing location problem in trip chain data: A POI-based probabilistic function method using open data

Abstract

Trip chaining captures integrated travel patterns besides commuting trips, uncovering activity participation. This study introduces a Point of Interest (POI)-based time-geography methodology to approximate geographic information in trip chaining in travel surveys in a computationally-efficient way. To demonstrate the applicability of this method, travel survey data from Eskilstuna, Sweden, and OpenStreetMap network data are used to achieve trip chain reconstruction with reduced computing time as compared to time geographic density estimation (TGDE). Reconstructed trip chain data provide a foundation for travel behavior pattern simulation, which is central to transport planning.