# Discovery of Important Crossroads in Road Network using Massive Taxi Trajectories

arXiv: https://arxiv.org/abs/1407.2506 · HTML: https://mlsanity.com/paper/1407.2506/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Jul 2014.

## Built on by (most cited first, 7 of 7)

- [Learning to Detect Critical Nodes in Sparse Graphs via Feature Importance Awareness](https://mlsanity.com/paper/2112.03404/) (2021)
- [Critical Nodes Identification in Complex Networks: A Survey](https://mlsanity.com/paper/2507.06164/) (2025)
- [Mining Topological Dependencies of Recurrent Congestion in Road Networks](https://mlsanity.com/paper/2107.09554/) (2021)
- [TraffNet: Learning Causality of Traffic Generation for What-if Prediction](https://mlsanity.com/paper/2303.15954/) (2023)
- [MGL2Rank: Learning to Rank the Importance of Nodes in Road Networks Based on Multi-Graph Fusion](https://mlsanity.com/paper/2305.14375/) (2023)
- [Learning to Rank Critical Road Segments via Heterogeneous Graphs with Origin-Destination Flow Integration](https://mlsanity.com/paper/2504.19199/) (2025)
- [Structure-Aware Graph Multi-Task Learning for Dynamic Sparse OD Demand Prediction](https://mlsanity.com/paper/2606.21022/) (2026)

## Abstract (arXiv)

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.