Discovery of Important Crossroads in Road Network using Massive Taxi Trajectories
This addresses a key issue in transport planning by enabling network-wide analysis of crossroads, though it appears incremental as it builds on existing graph-based methods with a new modeling twist.
The paper tackles the problem of identifying important crossroads across entire road networks by proposing CRRank, a data-driven approach that models trip demands with a tripartite graph and uses a HITS-like algorithm to rank crossroads, validated on real-world taxi trajectory data.
A major problem in road network analysis is discovery of important crossroads, which can provide useful information for transport planning. However, none of existing approaches addresses the problem of identifying network-wide important crossroads in real road network. In this paper, we propose a novel data-driven based approach named CRRank to rank important crossroads. Our key innovation is that we model the trip network reflecting real travel demands with a tripartite graph, instead of solely analysis on the topology of road network. To compute the importance scores of crossroads accurately, we propose a HITS-like ranking algorithm, in which a procedure of score propagation on our tripartite graph is performed. We conduct experiments on CRRank using a real-world dataset of taxi trajectories. Experiments verify the utility of CRRank.