An Approach to Bus Travel Time Prediction Based on the Adaptive Fading Kalman Filter Algorithm

CICTP 2012 ◽  
2012 ◽  
Author(s):  
Baojie Wang ◽  
Wei Wang ◽  
Min Yang ◽  
Liuyi Gao
2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Cong Bai ◽  
Zhong-Ren Peng ◽  
Qing-Chang Lu ◽  
Jian Sun

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.


2017 ◽  
Vol 11 (7) ◽  
pp. 362-372 ◽  
Author(s):  
B. Anil Kumar ◽  
R. Jairam ◽  
Shriniwas S. Arkatkar ◽  
Lelitha Vanajakshi

2019 ◽  
Vol 120 ◽  
pp. 426-435 ◽  
Author(s):  
Niklas Christoffer Petersen ◽  
Filipe Rodrigues ◽  
Francisco Camara Pereira

2019 ◽  
Vol 32 (14) ◽  
pp. 10435-10449 ◽  
Author(s):  
Chao Chen ◽  
Hui Wang ◽  
Fang Yuan ◽  
Huizhong Jia ◽  
Baozhen Yao

2020 ◽  
Vol 16 (3) ◽  
pp. 807-839 ◽  
Author(s):  
B. Dhivya Bharathi ◽  
B. Anil Kumar ◽  
Avinash Achar ◽  
Lelitha Vanajakshi

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