Rail accident analysis using large-scale investigations of train derailments on switches and crossings : comparing the performances of a novel stochastic mathematical prediction and various assumptions

Dindar, S; Kaewunruen, s; An, M

Abstract

Each day tens of turnout-related derailment occur across the world. Not only is the prediction of them quite complex and difficult, but this also requires a comprehensive range of applications, and managing a well-designed geographic information system. With the advent of Geographic Information Systems (GIS), and computers-aided solutions, the last two decades have witnessed considerable advances in the field of derailment prediction. Mathematical models with many assumptions and simulations based on fixed algorithms were also introduced to estimate derailment rates. While the former requires a costly investment of time and energy to try and find the most fitting mathematical solution, the latter is sometimes a high hurdle for analysts since the availability and accessibility of geospatial data are limited, in general. As train safety and risk
analysis rely on accurate assessment of derailment likelihood, a guide for transportation research is needed to show how each technique can approximate the number of observed derailments. In
this study, a new stochastic mathematical prediction model has been established on the basis of a hierarchical Bayesian model (HBM), which can better address unique exposure indicators in segmented large-scale regions. Integration of multiple specialized packages, namely, MATLAB for image processing, R for statistical analysis, and ArcGIS for displaying and manipulating geospatial data, are adopted to unleash complex solutions that will practically benefit the rail industry and transportation researchers.

Citation

Dindar, S., Kaewunruen, S., & An, M. (2019). Rail accident analysis using large-scale investigations of train derailments on switches and crossings : comparing the performances of a novel stochastic mathematical prediction and various assumptions. Engineering Failure Analysis, 103, 203-216. https://doi.org/10.1016/j.engfailanal.2019.04.010

• Journal Article Type: Article
• Acceptance Date: Apr 8, 2019
• Online Publication Date: Apr 15, 2019
• Publication Date: Sep 1, 2019
• Deposit Date: Jun 21, 2019
• Publicly Available Date: Apr 15, 2020
• Journal: Engineering Failure Analysis
• Print ISSN: 1350-6307
• Publisher: Elsevier
• Volume: 103
• Pages: 203-216
• DOI: https://doi.org/10.1016/j.engfailanal.2019.04.010
• Publisher URL: https://doi.org/10.1016/j.engfailanal.2019.04.010
• Related Public URLs: http://www.elsevier.com/locate/engfailanal
• Additional Information: Projects : Technology Research Innovations Grant Scheme