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 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.