Dynamic Identification of Stop Locations from GPS Trajectories Based on Their Temporal and Spatial Characteristics

The identification of stop locations in GPS trajectories is an essential preliminary step for obtaining trip information. We propose a neural network approach, based on the theoretical framework of dynamic neural fields (DNF), to identify automatically stop locations from GPS trajectories using their spatial and temporal characteristics. Experiments with real-world GPS trajectories were performed to show the feasibility of the proposed approach. The outcomes are compared with results obtained from more conventional clustering algorithms (K-means, hierarchical clustering, and HDBSCAN) which usually limit the use of the available temporal information to the definition of a threshold for the duration of stay. The experimental results show that the DNF approach not only robustly identifies places visited for a longer time but also stop locations that are visited for shorter periods but with higher frequency. Moreover, the self-stabilized activation patterns that the network dynamics develop and continuously update in response to GPS input encode simultaneously the spatial information and the time spent in each location. The impact of the obtained results on systems that automatically detect drivers' daily routines from GPS trajectories is discussed.