Mining Traffic Congestion Correlation between Road Segments on GPS Trajectories

Yuqi Wang, Jiannong Cao, Wengen Li, Tao Gu

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

22 Citations (Scopus)


Traffic congestion is a major concern in many cities around the world. Previous work mainly focuses on the prediction of congestion and analysis of traffic flows, while the congestion correlation between road segments has not been studied yet. In this paper, we propose a three-phase framework to study the congestion correlation between road segments from multiple real world data. In the first phase, we extract congestion information on each road segment from GPS trajectories of over 10,000 taxis, define congestion correlation and propose a corresponding mining algorithm to find out all the existing correlations. In the second phase, we extract various features on each pair of road segments from road network and POI data. In the last phase, the results of the first two phases are input into several classifiers to predict congestion correlation. We further analyze the important features and evaluate the results of the trained classifiers. We found some important patterns that lead to a high/low congestion correlation, and they can facilitate building various transportation applications. The proposed techniques in our framework are general, and can be applied to other pairwise correlation analysis.
Original languageEnglish
Title of host publication2016 IEEE International Conference on Smart Computing, SMARTCOMP 2016
ISBN (Electronic)9781509008988
Publication statusPublished - 28 Jun 2016
Event2nd IEEE International Conference on Smart Computing, SMARTCOMP 2016 - St. Louis, United States
Duration: 18 May 201620 May 2016


Conference2nd IEEE International Conference on Smart Computing, SMARTCOMP 2016
Country/TerritoryUnited States
CitySt. Louis


  • Classification
  • Congestion correlation
  • GPS trajectories
  • Traffic congestion

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Computer Networks and Communications
  • Urban Studies

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