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

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