Detecting spatiotemporal propagation patterns of traffic congestion from fine-grained vehicle trajectory data

Traffic congestion on a road segment typically begins as a small-scale spatiotemporal event that can then propagate throughout a road network and produce large-scale disruptions to a transportation system. In current techniques for the analysis of network flow, data is often aggregated to relatively large (e.g. 5 min) discrete time steps that obscure the small-scale spatiotemporal interactions that drive larger-scale dynamics. We propose a new method that handles fine-grained data to better capture those dynamics. Propagation patterns of traffic congestion are represented as spatiotemporally connected events. Each event is captured as a time series at the temporal resolution of the available trajectory data and at the spatial resolution of the network edge. The spatiotemporal propagation patterns of traffic congestion are captured using Dynamic Time Warping and represented as a set of directed acyclic graphs of spatiotemporal events. Results from this method are compared to an existing method using fine-grained data derived from an agent-based model of traffic simulation. Our method outperforms the existing method. Our method also successfully detects congestion propagation patterns that were reported by media news using sparse real-world data derived from taxis.