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A major problem in road network analysis is discovery of importantcrossroads, which can provide useful information for transport planning.However, none of existing approaches addresses the problem of identifyingnetwork-wide important crossroads in real road network. In this paper, wepropose a novel data-driven based approach named CRRank to rank importantcrossroads. Our key innovation is that we model the trip network reflectingreal travel demands with a tripartite graph, instead of solely analysis on thetopology of road network. To compute the importance scores of crossroadsaccurately, we propose a HITS-like ranking algorithm, in which a procedure ofscore propagation on our tripartite graph is performed. We conduct experimentson CRRank using a real-world dataset of taxi trajectories. Experiments verifythe utility of CRRank.

Discovery of Important Crossroads in Road Network using Massive Taxi Trajectories