多指标道路转向系统(RSSSM):智能车辆网络控制拥塞的一种新技术

Gaganpreet Kaur Marwah, Anuj.S Jain
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引用次数: 0

摘要

由于车辆和人口数量的增加,高速公路上的交通流量在过去几十年里急剧增加。交通拥堵是由固定的道路基础设施和车道上的过度交通造成的,特别是在发展中的国际城市。在主要城市,交通瓶颈是常见的,导致旅行时间增加,燃料使用增加,污染增加。本文提出了一种多指标道路转向系统(RSSSM),该系统使用多个指标分析交通拥堵情况,并根据这些条件为车辆提供有效的最佳路线。在智能车联网中使用SUMO和python脚本模拟了所建议的机制。根据交通效率、旅行时间、燃料消耗或污染程度等方面的数据,拟议的RSSSM机制优于现有的系统。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Road Steering system with several metrics (RSSSM): A novel technique for a Smart Vehicular Network to control congestion
Due to a growth in the number of vehicles and people, traffic flow on highways has expanded dramatically during the last few decades. Congestion is caused by fixed road infrastructure and excessive traffic on traffic lanes, especially in developing international cities. In major cities, traffic bottlenecks are common, resulting in increased travel time, increased fuel use, and increased pollution. This paper proposes a Road Steering system with several metrics(RSSSM) that analyses traffic congestion circumstances using many metrics and suggests efficient best routes to vehicles based on those conditions. The suggested mechanism is simulated in the smart vehicular network using SUMO and a python script. The suggested mechanism, RSSSM, beats existing systems, according to the data whether efficiency of traffic, duration of journey, consumption of fuel or level of pollutions are concerned.
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