A SELF-LEARNING ALGORITHM FOR PREDICTING BUS ARRIVAL TIME BASED ON HISTORICAL DATA MODEL

摘要：

　　The provision of timely and accurate bus arrive time information is very important.It helps to attract additional ridership and increase the satisfaction of transit users.In this paper,a self-learning prediction algorithm is proposed based on historical data model.Locations and speeds of the bus are periodically obtained from GPS senor installed on the bus and stored in database.Historical travel time in all road sections is collected.These historical data are trained using BP neural network to predict the average speed and arrival time of the road sections.Experimental results indicate that the proposed algorithm achieves outstanding prediction accuracy compared with general solutions based on historical travel time.

关键词： Bus arrival time prediction GPS BP neural network Historical data model

作者: Jian Pan Xiuting Dai Xiaoqi Xu Yanjun Li

作者单位: College of Computer Science and Technology,Zhejiang University of Technology,Hangzhou 310023,China

会议类型: 国际会议

会议名称: 2012 2nd IEEE International Conference on Cloud Computing and Intelligence Systems (2012年第2届IEEE云计算与智能系统国际会议(IEEE CCIS2012))

会议地点: 杭州

会议语种:英文

页码: 1541-1545

在线出版日期: 2012-10-30（万方平台首次上网日期，不代表论文的发表时间）