Research output per year
Research output per year
Abstract
Weather impacts on rail performance is complex. In this paper, we present an empirical data analysis of train delays, which were particularly attributed to wind, within the Anglia Route of Great Britain's railway network. This investigation involves a variety of data sources that are relevant to those railway incidents, including their detailed records, historic weather and trackside vegetation conditions and so on. Our work briefly explores how those data could be exploited in order to gain a deeper insight into the occurrence of the incidents, given the correlations between various factors, such as wind speed and tree density surrounding the incident sites. The survey findings will serve to underlie further analyses by using more advanced statistical approaches, for which specific recommendations are also made.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the International Conference on Railway Engineering (ICRE 2016) |
| Publisher | Institution of Engineering and Technology |
| Number of pages | 7 |
| ISBN (Electronic) | 9781785612930, 9781510832640 |
| ISBN (Print) | 9781785612923 |
| DOIs | |
| Publication status | Published - 12 May 2016 |
| Event | International Conference on Railway Engineering: ICRE 2016 - Brussels, Belgium Duration: 12 May 2016 → 13 May 2016 |
Conference
| Conference | International Conference on Railway Engineering |
|---|---|
| Country/Territory | Belgium |
| City | Brussels |
| Period | 12/05/16 → 13/05/16 |
Keywords
- Railway
- Big data
- Weather impact
- Wind
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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
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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Fu, Q. & Easton, J., 5 Jan 2018. 12 p.Research output: Contribution to conference (unpublished) › Paper › peer-review
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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 AccessFile3 Citations (Scopus)418 Downloads (Pure)