Identifying the most influential roads based on traffic correlation networks

Shengmin Guo, Dong Zhou, Jingfang Fan, Qingfeng Tong, Tongyu Zhu, Weifeng Lv, Daqing Li, Shlomo Havlin

Research output: Contribution to journalArticlepeer-review

9 Scopus citations


Prediction of traffic congestion is one of the core issues in the realization of smart traffic. Accurate prediction depends on understanding of interactions and correlations between different city locations. While many methods merely consider the spatio-temporal correlation between two locations, here we propose a new approach of capturing the correlation network in a city based on realtime traffic data. We use the weighted degree and the impact distance as the two major measures to identify the most influential locations. A road segment with larger weighted degree or larger impact distance suggests that its traffic flow can strongly influence neighboring road sections driven by the congestion propagation. Using these indices, we find that the statistical properties of the identified correlation network is stable in different time periods during a day, including morning rush hours, evening rush hours, and the afternoon normal time respectively. Our work provides a new framework for assessing interactions between different local traffic flows. The captured correlation network between different locations might facilitate future studies on predicting and controlling the traffic flows.

Original languageEnglish
Article number28
JournalEPJ Data Science
Issue number1
StatePublished - 1 Dec 2019
Externally publishedYes


  • Congestion propagation
  • Node importance
  • Traffic correlation network

ASJC Scopus subject areas

  • Modeling and Simulation
  • Computer Science Applications
  • Computational Mathematics


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