Mixed model for prediction of bus arrival times

The public transport information has been focus of social attention, especially bus arrival time (BAT) prediction. Historical data in combination with real-time data may be used to predict the future travel times of vehicles more accurately, thus improving the experience of the users who rely on such information. In this paper, we expound the correspondence among real-time data, history data and BAT. Hence, we propose short distance BAT prediction based on real-time traffic condition and long distance BAT prediction based on K Nearest Neighbors(KNN) respectively. Furthermore, original matching algorithm of KNN is modified for two times to accelerate matching procedure in terms of computationally expensive queries. In empirical studies with real data from buses, the model in this paper outperforms ANN or KNN used alone both in accuracy and efficiency of the algorithm, errors of which is less than 12 percent for a time horizon of 60 minutes.