MRGM: An Adaptive Mechanism for Congestion Control in Smart Vehicular Network
MRGM: An Adaptive Mechanism for Congestion Control in Smart Vehicular Network
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DOI：
10.54039/ijcnis.v12i2.4684
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发表时间：
2020-08
期刊：
影响因子：
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通讯作者：
Gurpreet singh Shahi;R. Batth;S. Egerton
中科院分区：
文献类型：
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作者：
Gurpreet singh Shahi;R. Batth;S. Egerton
由于车辆数量的增加和人口的增加，道路交通流量在过去几十年中成倍增加。由于固定的道路基础设施和交通路线上更多的车辆导致交通拥堵，尤其是在发展中国家的城市地区。交通拥堵在大城市是常态，最终会导致出行时间延误、燃油消耗增加和污染增加。本文提出了一种多指标道路引导机制（MRGM），该机制考虑多种指标来分析交通拥堵状况，并根据这些状况向车辆建议有效的最佳路线。使用Python脚本使用SUMO对所提出的机制进行仿真，结果表明所提出的机制即MRGM在智能车辆网络的交通效率、行程时间、燃料消耗和污染水平方面优于其他机制。
Traffic flow on roads has increased manifolds from past few decades due to increase in number of vehicles and rise in population. With fixed road infrastructure and more vehicles on traffic routes lead to traffic congestion conditions especially in urban areas of developing nations. Traffic jams are normal in major cities which ultimately cause delay in travel time, more fuel consumption and more pollution. This manuscript propose a Multi-metric road guidance mechanism(MRGM) which considers multiple metrics to analyze the traffic congestion conditions and based on the conditions effective optimal routes are suggested to the vehicles. The Simulation of the proposed mechanism is performed with the SUMO by using the python script and the results show that proposed mechanism i.e MRGM outperforms other mechanism in terms of traffic efficiency, travel time, fuel consumption and pollution levels in the smart vehicular network.