Hybrid model for prediction of bus arrival times at next station

TLDR

Effective prediction of bus arrival times is crucial for advanced traveler information systems (ATIS). The study proposes a hybrid model combining support vector machine and Kalman filtering to predict bus arrival times. The hybrid model uses SVM to estimate baseline travel times from historical data, weather, and route segments, and Kalman filtering to update predictions with real‑time arrival information, validated on bus 7 in a Dalian satellite town. The hybrid approach is feasible, applicable, and outperforms ANN‑based methods in bus arrival time forecasting. © 2010 John Wiley & Sons, Ltd.

Abstract

Abstract 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.