Welcome to the InfoSci Platform
Towards Intelligent Road Traffic Management Over a Weighted Large Graphs Hybrid Meta-Heuristic-Based Approach

Towards Intelligent Road Traffic Management Over a Weighted Large Graphs Hybrid Meta-Heuristic-Based Approach

Mohamed Yassine Hayi, Zahira Chouiref, Hamouma Moumen
Copyright: © 2022 |Volume: 24 |Issue: 3 |Pages: 18
ISSN: 1548-7717|EISSN: 1548-7725|EISBN13: 9781799878223|DOI: 10.4018/JCIT.20220701.oa4
Cite Article Cite Article

MLA

Hayi, Mohamed Yassine, et al. "Towards Intelligent Road Traffic Management Over a Weighted Large Graphs Hybrid Meta-Heuristic-Based Approach." JCIT vol.24, no.3 2022: pp.1-18. http://doi.org/10.4018/JCIT.20220701.oa4

APA

Hayi, M. Y., Chouiref, Z., & Moumen, H. (2022). Towards Intelligent Road Traffic Management Over a Weighted Large Graphs Hybrid Meta-Heuristic-Based Approach. Journal of Cases on Information Technology (JCIT), 24(3), 1-18. http://doi.org/10.4018/JCIT.20220701.oa4

Chicago

Hayi, Mohamed Yassine, Zahira Chouiref, and Hamouma Moumen. "Towards Intelligent Road Traffic Management Over a Weighted Large Graphs Hybrid Meta-Heuristic-Based Approach," Journal of Cases on Information Technology (JCIT) 24, no.3: 1-18. http://doi.org/10.4018/JCIT.20220701.oa4

Export Reference

Mendeley
Favorite Full-Issue Download

Abstract

This paper introduces a new approach of hybrid meta-heuristics based optimization technique for decreasing the computation time of the shortest paths algorithm. The problem of finding the shortest paths is a combinatorial optimization problem which has been well studied from various fields. The number of vehicles on the road has increased incredibly. Therefore, traffic management has become a major problem. We study the traffic network in large scale routing problems as a field of application. The meta-heuristic we propose introduces new hybrid genetic algorithm named IOGA. The problem consists of finding the k optimal paths that minimizes a metric such as distance, time, etc. Testing was performed using an exact algorithm and meta-heuristic algorithm on random generated network instances. Experimental analyses demonstrate the efficiency of our proposed approach in terms of runtime and quality of the result. Empirical results obtained show that the proposed algorithm outperforms some of the existing technique in term of the optimal solution in every generation.