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

Abstract

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

Keywords

• Big data
• data science
• gradient-boosting
• machine learning
• multi-layer perceptron
• neural network