This repo presents data models for predicting weather-related incidents on GB's rail network. Two case study examples are demonstrated in the context of delays caused primarily by high winds and high temperatures, respectively.
The prototype data model for the wind-related rail incidents was initially explored in a research project which was intended to establish a modelling framework for establishing the value of existing data resources as supporting data sets for improved decision-making in the UK rail industry. The methodology was firstly demonstrated in the context of delays as a result of high-wind events on the rail network. This case study examined the sub-networks most susceptible to the wind-related delays; and it identified appropriate data sources that could support the decision-making process around placement of trackside weather stations to monitor the impact of the high wind speeds.
The study investigated a variety of data sources, including historical records of rail incidents (attributed to poor weather), and weather observations and vegetation conditions in the vicinity of the incident locations. It showed on how those data could be modelled with logistic regression analysis, based on data integration in both temporal and spatial contexts. A number of factors contributing to the wind- / heat-related incidents have been identified and then used for making predictions of the occurrences of such incidents.
The prototype data models are generic and should also be adaptable to other categories of weather-related incidents, or a wider range of common industry tasks.
- Fu, Q. and Easton, J. M. (2018), Prediction of weather-related incidents on the rail network: prototype data model for wind-related delays in Great Britain. ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering 4(3): 04018027. doi:10.1061/AJRUA6.0000975.
- Fu, Q. and Easton, J. M. (2018), Establishing the value of existing data sources as supporting datasets for improved decision-making. Approved by Network Rail. (Unpublished)
- Fu, Q. Understanding and predicting weather-related incidents on the rail network: case studies of wind-/heat-related incidents. Invited seminar at the Institute for Risk and Uncertainty, University of Liverpool, Liverpool, UK, 8 January 2019.
- Jaroszweski, D. J., Fu, Q. and Easton, J. M. (2018), A data model for heat-related rail buckling: implications for operations, maintenance, presented at the upcoming 12th World Congress on Railway Research, Tokyo, Japan, 28 October - 1 November 2019. (Unpublished.)
- Fu, Q., Jaroszweski, D. J. and Easton, J. M. (2018), A prototype model for understanding heat-related rail incidents: a case study on the Anglia area in Great Britain, presented at the 8th International Conference on Railway Engineering, IET London: Savoy Place, 16-17 May 2018 [Presentation] [Q&A] (Access Code for the presentation and Q&A session can be provided upon request). Published in the Proceedings of the 8th International Conference on Railway Engineering, Institution of Engineering and Technology. doi:10.1049/cp.2018.0071.
- Fu, Q. and Easton, J. M. (2018), 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, presented at the 50th Annual Conference of the Universities’ Transport Study Group, London, UK, 3-5 January 2018. (Unpublished.)
- Fu, Q. and Easton, J. M. (2016), How does existing data improve decision making? A case study of wind-related incidents on rail network in Great Britain, presented at the 2016 International Conference on Railway Engineering, Brussels, Belgium, 12-13 May 2016 [Presentation]. Published in the Proceedings of the International Conference on Railway Engineering (ICRE 2016), Institution of Engineering and Technology. doi:10.1049/cp.2016.0515.
- Fu, Q. (2020). PyHelpers: an open-source toolkit for facilitating Python users' data manipulation tasks. doi:10.5281/zenodo.4081634
- Fu, Q. (2020). PyRCS: an open-source tool for collecting railway codes used in different UK rail industry systems. doi:10.5281/zenodo.4026744
- Fu, Q. (2020). PyDriosm: an open-source tool for downloading, reading and PostgreSQL-based I/O of OpenStreetMap data. doi:10.5281/zenodo.4281194