Real-Time Passenger Train Delay Prediction Using Machine Learning: A Case Study With Amtrak Passenger Train Routes

Passenger train delay significantly influences riders’ decision to choose rail transport as their mode choice. This article proposes real-time passenger prediction (PTDP) models using the following machine learning techniques: random forest (RF), gradient boosting (GBM), and multi-layer perceptron (MLP). In this article, impact on PTPD Real-time based Data-frame Structure (RT-DFS) with Historical (RWH-DFS) is investigated. The results show that PTDP MLP RWH-DFS outperformed all other models. influence of external variables such historical profiles at destination (HDPD), ridership, population, day week, geography, weather information are also further analyzed discussed.