To improve the accuracy of bus arrival time prediction, a Particle Filter (PF) algorithm with stream computing characteristic was used to establish a bus arrival time prediction model. In order to solve the problems of prediction error and particle optimization in the process of using PF algorithm, the prediction model was improved by introducing the latest bus speed and constructing observations, making it predict bus arrival time closer to the actual road conditions and simultaneously predict the arrival times of multiple buses. Based on the above model and Spark platform, a real-time bus arrival time prediction software system was implemented. Compared with actual results, for the off-peak period, the maximum absolute error was 207 s, and the mean absolute error was 71.67 s; for the peak period, the maximum absolute error was 270 s, and the mean absolute error was 87.61 s. The mean absolute error of the predicted results was within 2 min which was a recognized ideal result. The experimental results show that the proposed model and implementated system can accurately predict bus arrival time and meet passengers' actual demand.