Next Road Rerouting: A Multiagent System for Mitigating Unexpected Urban Traffic Congestion

Shen Wang, Soufiene Djahel, Zonghua Zhang, Jennifer McManis

Research output: Contribution to journalArticlepeer-review

109 Citations (Scopus)


During peak hours in urban areas, unpredictable traffic congestion caused by en route events (e.g., vehicle crashes) increases drivers' travel time and, more seriously, decreases their travel time reliability. In this paper, an original and highly practical vehicle rerouting system, which is called Next Road Rerouting (NRR), is proposed to aid drivers in making the most appropriate next road choice to avoid unexpected congestions. In particular, this heuristic rerouting decision is made upon a cost function that takes into account the driver's destination and local traffic conditions. In addition, the newly designed multiagent system architecture of NRR allows the positive rerouting impacts on local traffic to be disseminated to a larger area through the natural traffic flow propagation within connected local areas. The simulation results based on both synthetic and realistic urban scenarios demonstrate that, compared with the existing solutions, NRR can achieve a lower average travel time while guaranteeing a higher travel time reliability in the face of unexpected congestion. The impacts of NRR on the travel time of both rerouted and nonrerouted vehicles are also assessed, and the corresponding results reveal its higher practicability.

Original languageEnglish
Pages (from-to)2888-2899
Number of pages12
JournalIEEE Transactions on Intelligent Transportation Systems
Issue number10
Publication statusPublished - 14 Mar 2016
Externally publishedYes


  • road traffic congestion
  • unexpected en route events
  • multiagent system
  • vehicle rerouting


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