© The Institution of Engineering and Technology
RSSB and the University of Southampton's GeoData Institute have collaborated to research and develop a toolkit for managing large volumes of rail risk data. The pilot system encompasses concepts of highly complex geospatial ‘big data’, open standards, open source development tools and methodologies, and enables stakeholders to filter, analyse and visualise risk across the rail network, for a range of risk models. These include train derailments, suicides and passenger slip, trips and falls, and feature a wide range of spatially dependent parameters that affect the causal, escalation and consequence mechanisms. The risk has been calculated to a high resolution, splitting 2,100,000 m of track typically into 10 m sections. By creating geospatial representations of risk, the tool can help to identify risk hotspots and in this way contribute to the improvement of rail safety. Once scaled up to a National level and full range of risk models, the tool will deliver a powerful capability, unique across Europe. Further research is extending the prototype to incorporate live and historic environmental and related rail incident data to augment and improve the risk model.
References
-
-
1)
-
13. Wheater, H.S., Chandler, R.E., Onof, C.J., et al: ‘Spatial-temporal rainfall modelling for flood risk estimation’, Stoch. Environ. Res. Risk Assess., 2005, 19, (6), pp. 403–416 (doi: 10.1007/s00477-005-0011-8).
-
2)
-
9. Puri, K., Areendran, G., Raj, K., et al: ‘Forest fire risk assessment in parts of Northeast India using geospatial tools’, J. Forestry Res., 2011, 22, (4), pp. 641–647 (doi: 10.1007/s11676-011-0206-4).
-
3)
-
17. Lord, D., Geedipally, S., Guikema, S.: ‘Extension of the application of Conway-Maxwell-Poisson models: analyzing traffic crash data exhibiting underdispersion’, Risk Anal., 2010, 30, (8), pp. 1268–1276 (doi: 10.1111/j.1539-6924.2010.01417.x).
-
4)
-
1. McNulty, R.: ‘Realising the potential of GB rail, final independent report of the rail value for money study’. , 2011.
-
5)
-
19. Bedford, T., Cooke, R.: ‘Probabilistic risk analysis’ (Cambridge University Press, Cambridge).
-
6)
-
14. Van der Perk, M., Burrough, P.A., Voigt, G.: ‘GIS-based modelling to identify regions of Ukraine, Belarus and Russia affected by residues of the Chernobyl nuclear power plant accident’, J. Hazardous Mater., 1998, 61, (1/3), pp. 85–90 (doi: 10.1016/S0304-3894(98)00111-3).
-
7)
-
10. Thiebes, B., Bell, R., Glade, T., et al: ‘Integration of a limit-equilibrium model into a landslide early warning system’, J. Int. Consortium Landslides, 2014, 11, (5), pp. 859–875.
-
8)
-
6. Dennis, C., Somaiya, K.: ‘Development and Use of the UK Railway Network's Safety Risk Model’. Probabilistic Safety Assessment and Management: PSAM 7 – ESREL '04, Berlin, 2004.
-
9)
-
31. Loveridge, F., Spink, T., Briggs, K., et al: ‘The impact of climate and climate change on infrastructure slopes, with particular reference to southern England.’, Q J. Eng. Geol. Hydrogeol., 2010, 43, (4), pp. 461–472 (doi: 10.1144/1470-9236/09-050).
-
10)
-
11)
-
25. Tavistock Institute: ‘Improving suicide prevention methods on the rail network in Great Britain, annual report’. RSSB, London, 2013.
-
12)
-
29. Cheng, D.: ‘Uncertainty analysis of large risk assessment models with applications to the rail safety & standards board safety risk model’ (Strathclyde University, Glasgow, 2009).
-
13)
-
15. Nelder, J.A., Wedderburn, R.W.M.: ‘Generalized linear models’, J. R. Stat. Soc. A (General), 1972, 135, (3), pp. 370–384 (doi: 10.2307/2344614).
-
14)
-
3. Griffin, D., Holloway, A.: ‘Route based risk estimation across the GB rail network using empirical bayes methods’, ESREL, 2012.
-
15)
-
16)
-
16. Guikema, S.D., Goffelt, J.P.: ‘A flexible count data regression model for risk analysis’, Risk Anal., 2008, 28, (1), pp. 213–223 (doi: 10.1111/j.1539-6924.2008.01014.x).
-
17)
-
32. Orr, H., Carling, P.: .
-
18)
-
26. Evidence Led Solutions: ‘Improving suicide prevention measures on the rail network in Great Britain, literature review’ (RSSB, London, 2012).
-
19)
-
20)
-
18. Vesely, W., Goldberg, F., Roberts, N., et al: , 1981.
-
21)
-
2. Griffin, D.: ‘Geospatial modelling of rail safety hazards’ in Nowakowski, T., Młyńczak, M., Jodejko-Pietruczuk, A., Werbińska-Wojciechowska, S. (Eds.), ‘Safety and reliability: Methodology and Applications’, (london, 2014)pp. 1639–1649.
-
22)
-
27. SOVRN Project, Railway Suicide: ‘An investigation of individial and organisational consequences’, in Sheffield, J.W. (Ed.), (Northend Ltd, 2003).
-
23)
-
5. RSSB: ‘Safety risk model v8.1’, (London, 2014).
-
24)
-
7. Atkinson, P., Clark, M.J., Lewis, H.G.: ‘State-of-the-art in risk mapping’. Government Office for Science, Great Britain, 2012.
-
25)
-
22. : ‘Safety management information system’, (London, 2014).
-
26)
-
11. Pradhan, B., Mansor, S., Pirasteh, S., et al: ‘Landslide hazard and risk analyses at a landslide prone catchment area using statistical based geospatial model’, Int. J. Remote Sens., 2011, 32, (14), pp. 4075–4087 (doi: 10.1080/01431161.2010.484433).
-
27)
-
4. RSSB: ‘Taking safe decisions – How Britain's railways take decisions that affect safety’, (London, 2014).
-
28)
-
12. Towler, E., Roberts, M., Rajagopalan, B., et al: ‘Incorporating probabilistic seasonal climate forecasts into river management using a risk-based framework’, Water Resour. Res., 2013, 49, (8), pp. 4997–5008 (doi: 10.1002/wrcr.20378).
-
29)
-
30)
-
8. Barrett, K., Kasischke, E., Turetsky, M.A.D.M., et al: ‘Modeling fire severity in black spruce stands in the Alaskan boreal forest using spectral and non-spectral geospatial data’, Remote Sens. Environ., 2010, 114, (7), pp. 1494–1503 (doi: 10.1016/j.rse.2010.02.001).
-
31)
-
32)
-
30. Pennington, C., Dijkstra, T., Lark, M., et al: ‘Antecedent precipitation as a potential proxy for landslide incidence in South West United Kingdom’, in Sassa, K., Canuti, P., Yin, Y., (Eds.), ‘Landslide Science for a Safer Geoenvironment’, (Springer, 2014), pp. 253–259.
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