Dynamic Bus Travel Time Prediction Models on Road with Multiple Bus Routes

被引:61
作者
Bai, Cong [1 ,2 ,3 ]
Peng, Zhong-Ren [2 ,3 ,4 ]
Lu, Qing-Chang [1 ,3 ]
Sun, Jian [1 ,2 ]
机构
[1] Shanghai Jiao Tong Univ, State Key Lab Ocean Engn, Shanghai 200240, Peoples R China
[2] Shanghai Jiao Tong Univ, Sch Naval Architecture Ocean & Civil Engn, Shanghai 200240, Peoples R China
[3] Shanghai Jiao Tong Univ, Ctr ITS & UAV Applicat Res, Shanghai 200240, Peoples R China
[4] Univ Florida, Dept Urban & Reg Planning, Gainesville, FL 32611 USA
基金
中国国家自然科学基金;
关键词
Forecasting - Roads and streets - Bus transportation - Dynamic models - Support vector machines - Time varying control systems - Information filtering - Travel time - Kalman filters;
D O I
10.1155/2015/432389
中图分类号
Q [生物科学];
学科分类号
07 ; 0710 ; 09 ;
摘要
Accurate and real-time travel time information for buses can help passengers better plan their trips and minimize waiting times. A dynamic travel time prediction model for buses addressing the cases on road with multiple bus routes is proposed in this paper, based on support vector machines (SVMs) and Kalman filtering-based algorithm. In the proposed model, the well-trained SVM model predicts the baseline bus travel times from the historical bus trip data; the Kalman filtering-based dynamic algorithm can adjust bus travel times with the latest bus operation information and the estimated baseline travel times. The performance of the proposed dynamic model is validated with the real-world data on road with multiple bus routes in Shenzhen, China. The results show that the proposed dynamic model is feasible and applicable for bus travel time prediction and has the best prediction performance among all the five models proposed in the study in terms of prediction accuracy on road with multiple bus routes.
引用
收藏
页数:9
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