Probabilistic potential path trees for visualizing and analyzing vehicle tracking data

Abstract

Vehicle tracking data are often used to explore human travel behavior and activity patterns. Time geography is a useful approach for analyzing such datasets, as it provides a means for identifying the set of possible routes and stops for a vehicle between known locations, which is termed a potential path tree. This research extends the utility of the time-geographic approach by developing a method to generate probabilistic potential path trees that represent the space–time potential of a vehicle’s movements. First, this research provides the mathematical formulation of the new technique, network-based time-geographic density estimation (TGDE), and demonstrates the computation using a hypothetical tracking dataset and road network. Its formulation operates as a network adaptation of classical TGDE, which has been previously employed to analyze the movements of objects travelling in continuous, Euclidean space. Second, network-based TGDE is applied in the context of analyzing vehicle tracking data collected by GPS and filtered to protect an individual’s privacy. The method was used to map and quantify the vehicle’s most likely routes, origins, intermediate stops, and final destinations. The results indicate network-based time-geographic density estimation provides a powerful approach for both geovisualizing and analyzing vehicle tracking data.