期刊名称：International Journal of Innovative Research in Science, Engineering and Technology
印刷版ISSN：2347-6710
电子版ISSN：2319-8753
出版年度：2018
卷号：7
期号：4
页码：4062
DOI：10.15680/IJIRSET.2018.0704131
出版社：S&S Publications
摘要：As the number of vehicles grows rapidly each year, more and more traffic congestion occurs, becominga big issue for civil engineers in almost all metropolitan cities. In this paper, we introduce a Load-cell based trafficmanagement framework for reducing traffic congestion, which unifies the strategies of both dynamic vehicle reroutingand traffic light control. Specifically, each vehicle, represented as an agent, deposits Load-cell in its route, whileroadside infrastructure agents collect the Load-cell Information and fuse them to evaluate real-time traffic conditions aswell as to predict expected road congestion levels in near future. Once road congestion is predicted, a proactive vehiclererouting strategy based on global distance and local Load-cell is employed to assign alternative routes to selectedvehicles before they enter congested roads. In the meanwhile, traffic light control agents take online strategies to furtheralleviate traffic congestion levels. We propose and evaluate the traffic congestion problem and traffic light controlbased on load-cell information. The unified pheromone-based traffic management framework is compared with sevenother approaches in simulation environments. Experimental results show that the proposed framework outperformsother approaches in terms of traffic congestion levels and several other transportation metrics, such as air pollution andfuel consumption. Experiments over various compliance and penetration rates show the robustness of the proposedframework.
