Bus travel-time prediction based on bus speed

Bus travel-time prediction has attracted a lot of research interest in recent years. In the present study a support vector machine was applied to the bus travel-time prediction problem in an attempt to suggest a new model with better explanatory power and stability. Bus travel times forecast by the model were assessed using the data of transit route number 23 in Dalian, China. The results show that the support vector machine models outperformed the historic mean prediction model, the autoregressive integrated moving average and the artificial neural network model.