Institute of Transportation, MOTC

　　Short-term railway passenger demand forecasting can offer essential information to benefit short-term operational planning. This study constructed short-term forecasting models for railway passenger demand and discusses three modeling issues: the effects of input design on forecasting performance, validity of artificial neural networks and validity of combined models. We collected data from Taiwan Railway Administration for model construction and validation. Three findings were obtained. First, inappropriate design or use of input variables may result in unsatisfactory forecasting performance. Second, Artificial Neural Networks outperform random walk model, deseasonalized random walk model and moving average model, but have similar performance to exponential smoothing model. Third, combined models outperform individual models. However, candidates should be carefully selected for combining.