{"title":"Analysis and Control of Intelligent Traffic Signal System Based on Adaptive Fuzzy Neural Network","authors":"Changqing Dong, Kaixin Yang, Jinwei Guo, Xiaowei Chen, Haibo Dong, Yunlong Bai","doi":"10.1109/ICTIS.2019.8883791","DOIUrl":null,"url":null,"abstract":"The paper proposes an intelligent traffic signal control system based on AFNN (Adaptive Fuzzy Neural Network) algorithm, which can adjust the signal cycle and green split to improve traffic efficiency. First, based on V2X (Vehicle to X) intelligent networking technology, the numbers of waiting vehicles at traffic light intersections are real-timely detected. Then, an AFNN algorithm is used to get the knowledge of experience and online self-adjustment is followed according to traffic state to optimize light signal control scheme. Finally, the validity and rationality of the system are verified by the system simulation model. The results show that with the help of the adaptive control system, the average delay time was reduced by 8.45%, and the average fuel economy increased by 24.04%.","PeriodicalId":325712,"journal":{"name":"2019 5th International Conference on Transportation Information and Safety (ICTIS)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 5th International Conference on Transportation Information and Safety (ICTIS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICTIS.2019.8883791","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 4
Abstract
The paper proposes an intelligent traffic signal control system based on AFNN (Adaptive Fuzzy Neural Network) algorithm, which can adjust the signal cycle and green split to improve traffic efficiency. First, based on V2X (Vehicle to X) intelligent networking technology, the numbers of waiting vehicles at traffic light intersections are real-timely detected. Then, an AFNN algorithm is used to get the knowledge of experience and online self-adjustment is followed according to traffic state to optimize light signal control scheme. Finally, the validity and rationality of the system are verified by the system simulation model. The results show that with the help of the adaptive control system, the average delay time was reduced by 8.45%, and the average fuel economy increased by 24.04%.
本文提出了一种基于自适应模糊神经网络(AFNN)算法的智能交通信号控制系统，该系统可以调节信号周期和绿灯分割以提高交通效率。首先，基于V2X (Vehicle to X)智能网联技术，实时检测红绿灯路口等待车辆数量。然后，利用AFNN算法获取经验知识，根据交通状态进行在线自调整，优化光信号控制方案;最后，通过系统仿真模型验证了系统的有效性和合理性。结果表明，采用自适应控制系统后，平均延迟时间缩短8.45%，平均燃油经济性提高24.04%。
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