Hybrid model for prediction of bus arrival times at next station

• Published in: Journal of advanced transportation Vol. 44; no. 3; pp. 193 - 204
• Main Authors: Yu, Bin, Yang, Zhong-Zhen, Chen, Kang, Yu, Bo
• Format: Journal Article
• Language: English
• Published: Chichester, UK John Wiley & Sons, Ltd 01.07.2010; Wiley
• Subjects: Applied sciences; arrival time; Exact sciences and technology; Ground, air and sea transportation, marine construction; hybrid model; prediction; Road transportation and traffic; transportation; Arrival time; Road transportation; Bus; Numerical analysis; Mixed model; Kalman filtering; Forecast model; Bus stop; Comparative study; Passenger transportation
• ISSN: 0197-6729; 2042-3195
• DOI: 10.1002/atr.136

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.