Discovery of Critical Nodes in Road Networks Through Mining From Vehicle Trajectories

Road networks are extremely vulnerable to cascading failure caused by traffic accidents or anomalous events. Therefore, accurate identification of critical nodes, whose failure may cause a dramatic reduction in the road network transmission efficiency, is of great significance to traffic management and control schemes. However, none of the existing approaches can locate city-wide critical nodes in real road networks. In this paper, we propose a novel data-driven framework to rank node importance through mining from comprehensive vehicle trajectory data, instead of analysis solely on the topology of the road network. In this framework, we introduce a trip network modeled by a tripartite graph to characterize the dynamics of the road network. Furthermore, we present two algorithms, integrating the origin-destination entropy with flow (ODEF) algorithm and the crossroad-rank (CRRank) algorithm, to better exploit the information included in the tripartite graph and to effectively assess the node importance. ODEF absorbs the idea of the information entropy to evaluate the centrality of a node and to calculate its importance rating by integrating its centrality with the traffic flow. CRRank is a ranking algorithm based on eigenvector centrality that captures the mutual reinforcing relationships among the OD-pair, path, and intersection. In addition to the factors considered in ODEF, CRRank considers the irreplaceability of a node and the spatial relationships between neighboring nodes. We conduct a synthetic experiment and a real case study based on a real-world dataset of taxi trajectories. Experiments verify the utility of the proposed algorithms.