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Traffic Congestion Aware Route Assignment

Authors Sadegh Motallebi, Hairuo Xie, Egemen Tanin, Kotagiri Ramamohanarao

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Author Details

Sadegh Motallebi
  • The University of Melbourne, Australia
Hairuo Xie
  • The University of Melbourne, Australia
Egemen Tanin
  • The University of Melbourne, Australia
Kotagiri Ramamohanarao
  • The University of Melbourne, Australia

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Sadegh Motallebi, Hairuo Xie, Egemen Tanin, and Kotagiri Ramamohanarao. Traffic Congestion Aware Route Assignment. In 11th International Conference on Geographic Information Science (GIScience 2021) - Part I. Leibniz International Proceedings in Informatics (LIPIcs), Volume 177, pp. 9:1-9:15, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2020) https://doi.org/10.4230/LIPIcs.GIScience.2021.I.9

Abstract

Traffic congestion emerges when traffic load exceeds the available capacity of roads. It is challenging to prevent traffic congestion in current transportation systems where vehicles tend to follow the shortest/fastest path to their destinations without considering the potential congestions caused by the concentration of vehicles. With connected autonomous vehicles, the new generation of traffic management systems can optimize traffic by coordinating the routes of all vehicles. As the connected autonomous vehicles can adhere to the routes assigned to them, the traffic management system can predict the change of traffic flow with a high level of accuracy. Based on the accurate traffic prediction and traffic congestion models, routes can be allocated in such a way that helps mitigating traffic congestions effectively. In this regard, we propose a new route assignment algorithm for the era of connected autonomous vehicles. Results show that our algorithm outperforms several baseline methods for traffic congestion mitigation.

Subject Classification

ACM Subject Classification
  • Information systems → Geographic information systems
Keywords
  • Road Network
  • Traffic Congestion
  • Route Assignment
  • Shortest Path

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