Citation

Bai C, Peng ZR, Lu QC, Sun J. Comput. Intell. Neurosci. 2015; 2015: e432389.

Affiliation

State Key Laboratory of Ocean Engineering, Shanghai Jiao Tong University, Shanghai 200240, China ; School of Naval Architecture, Ocean and Civil Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.

Copyright

(Copyright © 2015, Hindawi Publishing)

DOI

10.1155/2015/432389

PMID

26294903

PMCID

PMC4534590

Abstract

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.

Language: en