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

Abstract

Given the complex traffic factors and strong mobility of a transit vehicles, it is difficult to predict its travel time with the conventional prediction models. The emphasis of this research effort is to improve the adaptability of the Kalman filter model with regard to transit bus travel time prediction. In order to resolve the filter divergence and calculated divergence in the prediction process, an adaptive fading Kalman filter algorithm is put forward in this paper. Compared with the conventional Kalman filter prediction model, a “forgotten factor” is added to the adaptive fading Kalman filter algorithm to restrain the influence of the old data on the filter. Furthermore, it can correct the predictive value and the model parameters with the latest measured value, hence higher adaptability and predictive accuracy. Finally, the GPS data of transit vehicles in the city of Yichun is used in the paper to verify the effectiveness of the new model. The results reveal that the adaptive fading Kalman filter algorithm is more effective than the conventional one. It performs better in terms of convergence and dynamic adaptability, implying a brighter application prospect.