The Adaptive Road Routing Recommendation for Traffic Congestion Avoidance in Smart City

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• 作者：Sheng-Tzong Cheng (1)
  Jian-Pan Li (1)
  Gwo-Jiun Horng (2)
  Kuo-Chuan Wang (1)
  
• 关键词：FNN ; Intersection ; delay prediction ; Traffic ; signal control ; Traffic congestion
• 刊名：Wireless Personal Communications
• 出版年：2014
• 出版时间：July 2014
• 年：2014
• 卷：77
• 期：1
• 页码：225-246
• 全文大小：
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• 作者单位：Sheng-Tzong Cheng (1)
  Jian-Pan Li (1)
  Gwo-Jiun Horng (2)
  Kuo-Chuan Wang (1)
  
  1. Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan, Taiwan
  2. Department of Information Management, Fortune Institute of Technology, Kaohsiung, Taiwan
  
• ISSN：1572-834X

文摘

A fuzzy neural network (FNN) calculates the traffic-light system and extends or terminates the green signal according to the traffic situation at the given junction while also computing from adjacent intersections. In the presence of public transports, the system decides which signal(s) should be red and how much of an extension should be given to green signals for the priority-based vehicle. Using fuzzy logic, we propose a model with a neural network for public transport, normal cars, and motorcycles. The model controls traffic-light systems to reduce traffic congestion and help vehicles with high priority pass through. The system also monitors the density of car flows and makes real-time decisions accordingly. In order to verify the proposed design algorithm, we adapted the simulations of Simulation of Urban MObility, ns2, and green light district simulation method to our model, and further results depict the performance of the proposed FNN in handling traffic congestion and priority-based traffic. The promising results present the efficiency and the scope of the proposed multi-module architecture for future development in traffic control.