Journal of South China University of Technology (Natural Science Edition) ›› 2017, Vol. 45 ›› Issue (3): 35-41,47.doi: 10.3969/j.issn.1000-565X.2017.03.005

• Electronics, Communication & Automation Technology • Previous Articles     Next Articles

A KMP-RBF Fusion Method to Forecast Duty Vehicle's Travel Time

JIN Shan1,2 JIN Zhi-gang1 LIU Yong-lei1   

  1. 1.School of Electrical and Information Engineering,Tianjin University,Tianjin 300072,China; 2.Fire Department of Tianjin,Tianjin 300020,China
  • Received:2016-05-06 Revised:2016-11-15 Online:2017-03-25 Published:2017-02-02
  • Contact: 金杉( 1982-) ，男，博士生，工程师，主要从事通信系统及工程、人工智能、无线传感器网络研究． E-mail:shanye2006@163.com
  • About author:金杉( 1982-) ，男，博士生，工程师，主要从事通信系统及工程、人工智能、无线传感器网络研究．
  • Supported by:
    Supported by the National Natural Science Foundation of China ( 61201179) and the National Postdoctoral Foundation( 2016M601265)

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Abstract:

Proposed in this paper is a KMP-RBF fusion method for forecasting the travel time of duty vehicle.In this method,the signal source consisting of GPS information and SCATS ( Sydney Coordinated Adaptive Traffic System) is utilized to establish a traffic information fusion model that combines fuzzy inference knowledge representation,MAPSO ( Multi-Agent Particle Swarm Optimization) and RBF ( Radial Basis Function) training together,the key parameters are optimized adaptively,and the time and space data are matched and obtained from historical training database.Experimental results show that the travel time after fusion and prediction is identical to the actual data measured by the traffic monitoring system,and that the proposed KMP-RBF fusion method is effective and reliable in the aspects of error rate，iterative degree and accuracy.

Key words: information fusion, forecasting, fuzzy inference, multi-agent particle swarm optimization algorithm, RBF networks, k-means algorithm, duty vehicle's travel time