A Two-phase BP Neural Network Method to Predict Average Delay of Signalized Intersection Under Multi-saturation Traffic States
Urban intersections average delay is a kind of basic data of the modern intelligent transportation system (ITS), used in real-time navigation, emergency traffic management and signal control. In this paper a Two-phase Back-Propagation (TBP) neural network model is introduced, which takes real-time volume, average speed and time occupancy as its inputs and outputs the intersections average delay of the next time step. An important characteristic is its flexibility to multi-saturation traffic states. The method is tested and verified using data from VISSIM simulation platform, which achieved satisfactory results.
Neural network Intelligent Transportation System (ITS) Intersection Delay Prediction
Yunkai Su Zuo Zhang Zhiheng Li Jun Ding Xiao Ma
Institute of Systems Engineering, Department of Automation, Tsinghua University, Beijing, 100084, Ch Institute of Systems Engineering, Department of Automation,Tsinghua University,Beijing,100084,China
国际会议
2011 China Control and Decision Conference(2011中国控制与决策会议 CCDC)
四川绵阳
英文
3878-3883
2011-05-23(万方平台首次上网日期,不代表论文的发表时间)