执勤行车时间的KMP-RBF融合预测方法  

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

作　　者：金杉[1,2] 金志刚[1] 刘永磊[1] JIN Shan JIN Zhi- gang LIU Yong-lei(School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China Fire Department of Tianjin, Tianjin 300020, China)

机构地区：[1]天津大学电气自动化与信息工程学院,天津300072 [2]天津市公安消防局,天津300020

出　　处：《华南理工大学学报（自然科学版）》2017年第3期35-41,47,共8页Journal of South China University of Technology(Natural Science Edition)

基　　金：国家自然科学基金资助项目(61201179);国家博士后科学基金资助项目(2016M601265)~~

摘　　要：针对执勤车辆沿某一路径行驶时长难以预测的问题,文中提出一种KMP-RBF融合方法,采用GPS定位、悉尼自适应交通控制系统(SCATS)线圈作为融合信号源,建立路况信息选择融合模型,将模糊推理知识表达、MAPSO算法寻优和RBF网络训练相结合,自适应优化系统关键参数,从训练数据库匹配适用时间、空间数据.实验中,使用交通监控系统实测实验用车行驶时长数据,并与预测数据进行对比,从误差率、算法迭代与精确度方面证明文中方法是高效而可靠的.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 Sys-tem） is utilized to establish a traffic information fusion model that combines fuzzy inference knowledge representa-tion, 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 reli-able in the aspects of error rate, iterative degree and accuracy.

关 键 词：信息融合 预测 模糊推理 多智能体粒子群优化算法 RBF神经网络 K-均值算法 执勤行车时间 

分 类 号：TP202[自动化与计算机技术—检测技术与自动化装置]