Abstract: This paper studies bus operation in China and presents a short-term bus arrival time prediction model using fuzzy neural network (FNN) with real-time traffic information collected by a global positioning system and an electronic payment system. Road segment and bus stop based FNN are proposed to predict bus travel time on road segments and time spent at bus stops. As opposed to previous studies, the present approach considers traffic data collected at bus stops with overlapping multiple bus routes instead of at those with single routes. Taking a bus artery in Jinan city as an example, this paper conducted intensive numerical experiments with simulated data from the microscopic simulator VISSIM in which both exclusive and non-exclusive bus lanes were simulated. Cumulative errors of predicted travel time were under 10% in bus non-exclusive and less than 7% in bus exclusive lanes when the prediction time window was less than 15 minutes, indicating that the proposed approach significantly outperforms the mean value method and the Kalman filtering model.