Imperial College London
Alternate

Dr Simon Hu

Faculty of Engineering, Department of Civil and Environmental Engineering

Honorary Research Fellow
 
 
 
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Contact

 

+44 (0)20 7594 6024j.s.hu05

 
 
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Location

 

422Skempton BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Taleongpong:2020:10.1080/15472450.2020.1858822,
author = {Taleongpong, P and Hu, S and Jiang, Z and Wu, C and Popo-Ola, S and Han, K},
doi = {10.1080/15472450.2020.1858822},
journal = {Journal of Intelligent Transportation Systems: technology, planning, and operations},
pages = {1--19},
title = {Machine learning techniques to predict reactionary delays and other associated key performance indicators on British railway network},
url = {http://dx.doi.org/10.1080/15472450.2020.1858822},
volume = {2020},
year = {2020}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - 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.
AU  - Taleongpong,P
AU  - Hu,S
AU  - Jiang,Z
AU  - Wu,C
AU  - Popo-Ola,S
AU  - Han,K
DO  - 10.1080/15472450.2020.1858822
EP  - 19
PY  - 2020///
SN  - 1547-2450
SP  - 1
TI  - Machine learning techniques to predict reactionary delays and other associated key performance indicators on British railway network
T2  - Journal of Intelligent Transportation Systems: technology, planning, and operations
UR  - http://dx.doi.org/10.1080/15472450.2020.1858822
UR  - http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000604003100001&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
UR  - https://www.tandfonline.com/doi/full/10.1080/15472450.2020.1858822
UR  - http://hdl.handle.net/10044/1/85976
VL  - 2020
ER  -
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