Hybrid model for prediction of bus arrival times at next station
Version of Record online: 2 JUL 2010
Copyright © 2010 John Wiley & Sons, Ltd.
Journal of Advanced Transportation
Volume 44, Issue 3, pages 193–204, July 2010
How to Cite
Yu, B., Yang, Z.-Z., Chen, K. and Yu, B. (2010), Hybrid model for prediction of bus arrival times at next station. J. Adv. Transp., 44: 193–204. doi: 10.1002/atr.136
- Issue online: 18 JUL 2010
- Version of Record online: 2 JUL 2010
- arrival time;
- hybrid model;
Effective prediction of bus arrival times is important to advanced traveler information systems (ATIS). Here a hybrid model, based on support vector machine (SVM) and Kalman filtering technique, is presented to predict bus arrival times. In the model, the SVM model predicts the baseline travel times on the basic of historical trips occurring data at given time-of-day, weather conditions, route segment, the travel times on the current segment, and the latest travel times on the predicted segment; the Kalman filtering-based dynamic algorithm uses the latest bus arrival information, together with estimated baseline travel times, to predict arrival times at the next point. The predicted bus arrival times are examined by data of bus no. 7 in a satellite town of Dalian in China. Results show that the hybrid model proposed in this paper is feasible and applicable in bus arrival time forecasting area, and generally provides better performance than artificial neural network (ANN)–based methods. Copyright © 2010 John Wiley & Sons, Ltd.