Name
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. |
Year | Venue | Field |
|---|---|---|
2014 | CoRR | Graph,Data mining,Ranking,Computer science,Artificial intelligence,Network analysis,Transportation planning,Machine learning |
DocType | Volume | Citations |
Journal | abs/1407.2506 | 3 |
PageRank | References | Authors |
0.48 | 6 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ming Xu | 1 | 10 | 2.62 |
| Jianping Wu | 2 | 54 | 8.57 |
| Yiman Du | 3 | 20 | 2.50 |
| Haohan Wang | 4 | 32 | 7.26 |
| Geqi Qi | 5 | 3 | 0.82 |
| Kezhen Hu | 6 | 3 | 0.82 |
| Yunpeng Xiao | 7 | 19 | 1.43 |