Discovery of Important Crossroads in Road Network using Massive Taxi Trajectories

TL;DR

This paper introduces CRRank, a data-driven method that models real travel demands with a tripartite graph and uses a HITS-like algorithm to identify important crossroads in road networks, aiding transport planning.

Contribution

The paper presents a novel tripartite graph model and a HITS-like ranking algorithm for network-wide importance of crossroads, addressing limitations of previous topology-only analyses.

Findings

CRRank effectively identifies important crossroads in real-world taxi data.

Experiments demonstrate CRRank's utility and accuracy in ranking crossroads.

The approach outperforms traditional topology-based methods.

Abstract

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.