  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.
