Train delays prediction based on feature selection and random forest

Abstract

Although trains are more efficient and convenient than other transportation, delays often occur. Accurately predicting the delay time of trains is of great significance to both dispatchers and passengers. The method for predicting the arrival delay time of trains is based on feature selection algorithm and machine learning. First, we collect train delay cases to sort out the delay factors. In addition to internal factors, external factors such as weather and signal failure are also considered. Then, an improved max-relevance and min-redundancy method (mRMR) is used for feature selection. Finally, we apply the method of weighted random forest (wRF) to predict the delay time. The results demonstrate that the feature selection algorithm has a prominent effect on improving the accuracy of the model, and the mean square error based on the weighted random forest has an improvement potential in forecast precision.