Arrival time prediction and train tracking analysis

Abstract

Rail transportation is a convenient and safe in many countries. However, Rail transportation in some countries has significant long delays. Arrival time prediction and rescheduling the time table are partial solutions to tackle the delay problem. In this paper, the relationship between measurable properties and the delay time are studied in order to develop an arrival time prediction. The result of this experiment has three parts. The relationship between properties and arrival late are then visualized and discussed. Some properties from the acquired database show that week, day and station, are important features and impact on the delay. Various regression methods are compared in our experiment and the result shows that best RMSE is ± 3.863 min by applying Random Forest Regression on train tracking dataset.