Machine learning techniques to predict reactionary delays and other associated key performance indicators on British railway network. Issue 3 (4th May 2022)

• Record Type: Journal Article
• Authors: Taleongpong, Panukorn; Hu, Simon; Jiang, Zhoutong; Wu, Chao; Popo-Ola, Sunday; Han, Ke
• Is Part Of: Journal of intelligent transportation systems. Volume 26:Issue 3(2022)
• Page Start: 311
• Page End: 329
• Publication Date: 2022-05-04
• Subjects: gradient boosting -- machine learning -- railway delay prediction -- reactionary delay; Intelligent transportation systems -- Periodicals; Transportation -- Technological innovations -- Periodicals; 388.312
• DOI: 10.1080/15472450.2020.1858822
• Languages: English
• ISSNs: 1547-2450

Abstract

Abstract: Reactionary delays that propagate from a primary source throughout train journeys are an immediate concern for British railway systems. Complex non-linear interactions between various spatiotemporal variables govern the propagation of these delays which can avalanche throughout railway network causing further severe disruptions. This paper introduces several machine learning (ML) techniques alongside data mining processes to create a framework that predicts key performance indicators (KPIs), reactionary arrival delay, reactionary departure delay, dwell time and travel time. The frameworks in this paper provide greater accuracy in predicting KPIs through state-of-the-art ML models compared to existing delay prediction systems. Further discussion on the improvements, applicability and scalability of this framework are also provided in this paper.