Nowadays, the increase in the number of vehicles on the roads has brought about several problems such as an increase in traffic congestion and, consequently, in polluting emissions. These problems are especially severe in urban environments. It is crucial to perform a sustainable urban mobility plan to improve the traffic and therefore, reduce the negative impacts caused by traffic jams. To this end, this paper presents a smart mobility plan that employs a collaborative driving strategy. Each vehicle tries to infer traffic conditions using its own status and the information shared by other peers. Using a fuzzy logic approach, vehicles perform decisions in accordance with the traffic levels inferred in real time. The designed mobility plan has been tested through a simulation environment and considering two types of urban areas in a typical European city (a peripheral area and a more congested city centre). If we compare the performance of traffic with and without the system designed, with our approach average speeds increase by up to 11.20 % and CO emissions are reduced by up to 12.27 %. Thus, our results show that the mobility plan has helped to enhance the ability of cars to be able to solve problems caused by traffic congestion and traffic jams.

中文翻译：

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使用模糊逻辑和 V2X 通信改善交通状况的分布式协作系统

如今，道路上车辆数量的增加带来了一些问题，例如交通拥堵加剧，从而导致污染排放增加。这些问题在城市环境中尤其严重。执行可持续的城市交通计划以改善交通并减少交通拥堵造成的负面影响至关重要。为此，本文提出了一种采用协作驾驶策略的智能出行计划。每辆车都尝试使用自己的状态和其他同行共享的信息来推断交通状况。使用模糊逻辑方法，车辆根据实时推断的交通水平进行决策。设计的交通计划已经通过模拟环境进行了测试，并考虑了典型欧洲城市的两种类型的城市区域（外围区域和更拥挤的市中心）。如果我们比较有设计的系统和没有设计的系统的交通性能，我们的方法平均速度提高了 11.20%，二氧化碳排放量减少了 12.27%。因此，我们的结果表明，移动计划有助于增强汽车解决交通拥堵和拥堵问题的能力。