Spatio-temporal Association Rule Mining of Traffic Congestion in a Large-scale Road Network Based on Trajectory Data

Authors

Qifan Zhou, School of Transportation and Logistics, Southwest Jiaotong University, Chengdu 611756, ChinaFollow
Haixu Liu, School of Transportation and Logistics, Southwest Jiaotong University, Chengdu 611756, China; National Engineering Laboratory of Application Technology of Integrated Transportation Big Data, Southwest Jiaotong University, Chengdu 611756, China; National United Engineering Laboratory of Integrated and Intelligent Transportation, Southwest Jiaotong University, Chengdu 610031, China; Comprehensive Transportation Key Laboratory of Sichuan Province, Southwest Jiaotong University, Chengdu 611756, ChinaFollow
Zhipeng Dong, School of Transportation and Logistics, Southwest Jiaotong University, Chengdu 611756, China
Yin Xu, School of Transportation and Logistics, Southwest Jiaotong University, Chengdu 611756, China

Abstract

Abstract: A K neighbor-RElim (KNR) algorithm and a sequential KNbr-RElim (SKNR) algorithm are proposed to mine traffic congestion association rules and congestion propagation spatio-temporal association rules by vehicle trajectory data in a large-scale road network. The KNR algorithm extends the spatial topology constraint based on the RElim algorithm. The KNR can be used to mine the road links prone to congestion from the large-scale trajectory dataset in a large-scale road network and quantify the strength of association for congested road links. The SKNR algorithm expands the time dimension in the form of sliding window and can be applied for mining the congestion propagation phenomenon which is difficult to observe directly in a large-scale road network and tracing the path of congestion propagation. The algorithms are illustrated and verified by the empirical results of the Chengdu road network with vehicle trajectory data. The results show the effectiveness and robustness of the proposed algorithms.

Recommended Citation

Zhou, Qifan; Liu, Haixu; Dong, Zhipeng; and Xu, Yin (2024) "Spatio-temporal Association Rule Mining of Traffic Congestion in a Large-scale Road Network Based on Trajectory Data," Journal of System Simulation: Vol. 36: Iss. 1, Article 20.
DOI: 10.16182/j.issn1004731x.joss.22-0898
Available at: https://dc-china-simulation.researchcommons.org/journal/vol36/iss1/20

First Page

260

Last Page

271

CLC

TP301.6

Recommended Citation

Zhou Qifan, Liu Haixu, Dong Zhipeng, et al. Spatio-temporal Association Rule Mining of Traffic Congestion in a Large-scale Road Network Based on Trajectory Data[J]. Journal of System Simulation, 2024, 36(1): 260-271.

Corresponding Author

Liu Haixu

DOI

10.16182/j.issn1004731x.joss.22-0898