A spatial approach to anomalous barrier detection in transportation networks from movement trajectories

Dynamic information about barriers impeding traffic flow is becoming an essential part of transportation analysis in the context of disruptive events. The automated detection of barriers from individual or vehicular tracking data previously relied on various statistical models of traffic counts and geometric patterns of mobility paths. To account for the variance of traffic flow in peripheral parts of a network, often due to limited data or device coverage, we propose a barrier detection method using trajectory data based on the randomized shortest path model, a probabilistic model for trip paths. By overlaying the spatial distribution of multiple trips, we model traffic counts on each edge as a Poisson process and infer potential barriers from edges with unexpectedly low counts. Therefore, the proposed method incorporates a spatial criterion of path distribution and a statistical criterion of traffic counts for barrier detection. We validated the proposed method on both synthetic data and real-world data, by comparing it with methods using statistical and temporal criteria. Our results allow us to discuss the bias of randomized shortest path model for finite-size networks, the advantages of spatial and temporal criteria for this task, and the capability limitations of the proposed method.