A prototype model for understanding heat-related rail incidents: a case study on the Anglia area in Great Britain

Qian Fu; David Jaroszweski; John Easton

doi:10.1049/cp.2018.0071

A prototype model for understanding heat-related rail incidents: a case study on the Anglia area in Great Britain

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

In this paper, we present a prototype model based on logistic regression analysis, with the aim to understand better the impact of temperature on heat-related incidents on the rail network in Britain. The work draws support from climatic, geographical, and vegetation data resources, and investigates ways in which these data may be used to predict when and where heat-related incidents will most likely occur, thus enabling us to gain a deeper understanding of the conditions that prevail in sites at risk of heat-related disruption events on the rail network in Great Britain. The method is demonstrated using historical records of heat-related incidents within the Anglia area between 2006/07 and 2014/15. By considering a selection of variables, the initial results show that the prototype has good overall performance in terms of both understanding as well as prediction of heat-related incident occurrence.

Original language	English
Title of host publication	Proceedings of the 8th International Conference on Railway Engineering (ICRE 2018)
Publisher	Institution of Engineering and Technology
Number of pages	6
ISBN (Electronic)	9781785618468
DOIs	https://doi.org/10.1049/cp.2018.0071
Publication status	Published - 25 Jun 2018
Event	8th International Conference on Railway Engineering (ICRE 2018) - IET Savoy Place, London, United Kingdom Duration: 16 May 2018 → 17 May 2018 Conference number: CP742

Conference

Conference	8th International Conference on Railway Engineering (ICRE 2018)
Abbreviated title	ICRE 2018
Country/Territory	United Kingdom
City	London
Period	16/05/18 → 17/05/18

Keywords

Rail
High temperature
Heat-related incident
Logistic regression

Access to Document

10.1049/cp.2018.0071Licence: None: All rights reserved

http://digital-library.theiet.org/content/conferences/10.1049/cp.2018.0071Licence: None: All rights reserved

3 Software
2 Conference contribution
1 Paper
1 Article

PyDriosm: an open-source tool for downloading, reading and PostgreSQL-based I/O of OpenStreetMap data
Fu, Q., 2020
Research output: Non-textual form › Software

Open Access
PyHelpers: An open-source toolkit for facilitating Python users' data manipulation tasks
Fu, Q., 2020
Research output: Non-textual form › Software

Open Access
PyRCS: an open-source tool for collecting railway codes used in different UK rail industry systems
Fu, Q., 2020
Research output: Non-textual form › Software

Open Access
A data model for heat-related rail buckling: implications for operations, maintenance and long-term adaptation
Jaroszweski, D., Fu, Q. & Easton, J., 1 Aug 2019, (Accepted/In press) Proceedings of the 12th World Congress on Railway Research. 6 p.
Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Open Access
File
A data model for prediction of weather-related rail incidents: a case-study example of wind-related incidents on the rail network in Great Britain
Fu, Q. & Easton, J., 5 Jan 2018. 12 p.
Research output: Contribution to conference (unpublished) › Paper › peer-review
Prediction of weather-related incidents on the rail network: prototype data model for wind-related delays in Great Britain
Fu, Q. & Easton, J., Sept 2018, In: ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering. 4, 3, 12 p., 04018027.
Research output: Contribution to journal › Article › peer-review

Open Access
File
3 Citations (Scopus)

411 Downloads (Pure)
How does existing data improve decision making? A case study of wind-related incidents on rail network
Fu, Q. & Easton, J., 12 May 2016, Proceedings of the International Conference on Railway Engineering (ICRE 2016). Institution of Engineering and Technology, 7 p.
Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
1 Citation (Scopus)

Cite this

@inproceedings{51ca7bee64684db59faef841ca5e5e98,

title = "A prototype model for understanding heat-related rail incidents: a case study on the Anglia area in Great Britain",

abstract = "In this paper, we present a prototype model based on logistic regression analysis, with the aim to understand better the impact of temperature on heat-related incidents on the rail network in Britain. The work draws support from climatic, geographical, and vegetation data resources, and investigates ways in which these data may be used to predict when and where heat-related incidents will most likely occur, thus enabling us to gain a deeper understanding of the conditions that prevail in sites at risk of heat-related disruption events on the rail network in Great Britain. The method is demonstrated using historical records of heat-related incidents within the Anglia area between 2006/07 and 2014/15. By considering a selection of variables, the initial results show that the prototype has good overall performance in terms of both understanding as well as prediction of heat-related incident occurrence.",

keywords = "Rail, High temperature, Heat-related incident, Logistic regression",

author = "Qian Fu and David Jaroszweski and John Easton",

year = "2018",

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doi = "10.1049/cp.2018.0071",

language = "English",

booktitle = "Proceedings of the 8th International Conference on Railway Engineering (ICRE 2018)",

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note = "8th International Conference on Railway Engineering (ICRE 2018), ICRE 2018 ; Conference date: 16-05-2018 Through 17-05-2018",

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Fu, Q , Jaroszweski, D & Easton, J 2018, A prototype model for understanding heat-related rail incidents: a case study on the Anglia area in Great Britain. in Proceedings of the 8th International Conference on Railway Engineering (ICRE 2018). Institution of Engineering and Technology, 8th International Conference on Railway Engineering (ICRE 2018), London, United Kingdom, 16/05/18. https://doi.org/10.1049/cp.2018.0071

A prototype model for understanding heat-related rail incidents: a case study on the Anglia area in Great Britain. / Fu, Qian ; Jaroszweski, David ; Easton, John.
Proceedings of the 8th International Conference on Railway Engineering (ICRE 2018). Institution of Engineering and Technology, 2018.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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T1 - A prototype model for understanding heat-related rail incidents

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N2 - In this paper, we present a prototype model based on logistic regression analysis, with the aim to understand better the impact of temperature on heat-related incidents on the rail network in Britain. The work draws support from climatic, geographical, and vegetation data resources, and investigates ways in which these data may be used to predict when and where heat-related incidents will most likely occur, thus enabling us to gain a deeper understanding of the conditions that prevail in sites at risk of heat-related disruption events on the rail network in Great Britain. The method is demonstrated using historical records of heat-related incidents within the Anglia area between 2006/07 and 2014/15. By considering a selection of variables, the initial results show that the prototype has good overall performance in terms of both understanding as well as prediction of heat-related incident occurrence.

AB - In this paper, we present a prototype model based on logistic regression analysis, with the aim to understand better the impact of temperature on heat-related incidents on the rail network in Britain. The work draws support from climatic, geographical, and vegetation data resources, and investigates ways in which these data may be used to predict when and where heat-related incidents will most likely occur, thus enabling us to gain a deeper understanding of the conditions that prevail in sites at risk of heat-related disruption events on the rail network in Great Britain. The method is demonstrated using historical records of heat-related incidents within the Anglia area between 2006/07 and 2014/15. By considering a selection of variables, the initial results show that the prototype has good overall performance in terms of both understanding as well as prediction of heat-related incident occurrence.

KW - Rail

KW - High temperature

KW - Heat-related incident

KW - Logistic regression

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Y2 - 16 May 2018 through 17 May 2018

ER -

A prototype model for understanding heat-related rail incidents: a case study on the Anglia area in Great Britain

Abstract

Conference

Keywords

Access to Document

Fingerprint

Research output

PyDriosm: an open-source tool for downloading, reading and PostgreSQL-based I/O of OpenStreetMap data

PyHelpers: An open-source toolkit for facilitating Python users' data manipulation tasks

PyRCS: an open-source tool for collecting railway codes used in different UK rail industry systems

A data model for heat-related rail buckling: implications for operations, maintenance and long-term adaptation

A data model for prediction of weather-related rail incidents: a case-study example of wind-related incidents on the rail network in Great Britain

Prediction of weather-related incidents on the rail network: prototype data model for wind-related delays in Great Britain

How does existing data improve decision making? A case study of wind-related incidents on rail network

Cite this