Abstract: In order to improve the prediction accuracy of bus travel time, a combined prediction model based on artificial bee colony optimization and extreme learning machine (artificial bee colony-extreme learning machine, ABC-ELM) are proposed after analyzing historical data and the characteristics of traffic flow. First, dynamic and static characteristics like distance between stations, time period and weather conditions are extracted by using IC card and GPS data; after that the dwell time of the station is calculated. Then, the artificial bee colony optimization algorithm (ABC) is embedded in the traditional extreme learning machine algorithm (ELM) to solve the problem of slow convergence speed and difficulty in selecting initial weights and thresholds ELM in bus travel time prediction. Finally, the travel time of the bus on target road section is predicted by using the ABC-ELM algorithm. The model is verified based on the real operating data of Shenzhen Bus 620. The results show that, compared with the widely used BP neural network, SVM and ELM, the method proposed in this paper can maintain lower prediction errors in different road environments and has strong robustness (the RMSE error in peak/off-peak hour is 11.91/8.72, in workday/non-work day is 11.46/9.54,in sunny/rainy day is 10.83/12.31; the coefficient of determination R² in peak/off-peak hour is 0.87/0.92 in workday/non-work day is 0.83/0.88, in sunny/rainy day is 0.89/0.85), which makes it more suitable for travel time prediction in complex urban road environment and for main line bus.

Key words: urban traffic, public bus, travel time prediction, extreme learning machine, artificial bee colony algorithm