Research Article
A Stochastic Traffic Model for Congestion Detection in Multi-lane Highways
@INPROCEEDINGS{10.1007/978-3-030-67369-7_7, author={El Joubari Oumaima and Ben Othman Jalel and V\'{e}que V\^{e}ronique}, title={A Stochastic Traffic Model for Congestion Detection in Multi-lane Highways}, proceedings={Ad Hoc Networks. 12th EAI International Conference, ADHOCNETS 2020, Paris, France, November 17, 2020, Proceedings}, proceedings_a={ADHOCNETS}, year={2021}, month={1}, keywords={VANETs Mobility model Vehicular traffic Markov chain}, doi={10.1007/978-3-030-67369-7_7} }- El Joubari Oumaima
Ben Othman Jalel
Vèque Véronique
Year: 2021
A Stochastic Traffic Model for Congestion Detection in Multi-lane Highways
ADHOCNETS
Springer
DOI: 10.1007/978-3-030-67369-7_7
Abstract
Vehicular Ad Hoc Networks (VANETs) represent a significant leap forward in the deployment of intelligent transport systems. These networks enable vehicles to instantly exchange traffic information with the aim of smoothing traffic flows and intensifying drivers comfort. In this context, this study addresses the issue of traffic congestion description and detection in multi-lane highways. By making use of collected information, a Markov chain based mobility model is proposed to predict the future road traffic states. Based on the obtained stationary distribution probabilities, performance criteria in steady-state are inferred and computed for different road configurations. The numerical results validate the model demonstrated in the paper.
