Abstract
The European Community has developed a real turning point in the common rail transport policy by defining new ambitions to rebalance sustainably the sharing between modes of transport, develop intermodality, fight congestion, and finally place safety at the heart of European action. To consolidate the usual methods of railway safety analysis, this chapter proposes two complementary railway safety assessment methods based on AI techniques and in particular on machine learning (ML). The study seeks to exploit, by machine learning, the lessons resulting from “Experience Feedback” (REX) in order to help and assist safety experts, technical investigators, and certification bodies to assess the level of safety of a new rail transport system. Unfortunately, safety in rail transport improves essentially on the basis of in-depth knowledge of accidents and incidents resulting from “experience feedback”. As stipulated by European regulations, all players in rail transport and in particular infrastructure managers and railway undertakings are obliged to set up a system of “experience feedback” in order to understand the causes and the seriousness of the consequences engendered by rail accidents and incidents. So, the knowledge of accidents and incidents results essentially from the contribution of lessons learned and experiences acquired. To explain and understand the causes and circumstances of accident risks and therefore at least avoid the reproduction of similar accidents, we have oriented our study toward the use of approaches derived from AI and machine learning. From experience feedback, the main objective is to exploit a set of insecurity events in order to anticipate and prevent the reproduction of the risks of accidents or similar incidents and possibly to discover and identify new scenarios of potential accidents liable to jeopardize safety. This chapter proposes a new hybrid method based on three machine learning algorithms. The first stage of acquiring knowledge led to the development of two accident scenario databases. The first base relates to the analysis of “functional safety”, and the second base relates to the analysis of the “security of critical software”. The second step, which is based on a concept classification algorithm, makes it possible to group the accident scenarios into coherent classes such as the class relating to train collision or derailment problems. For each class of accident or incident scenarios, the third step implements a learning technique based on production rules in order to identify some relevant safety rules. In the fourth step, the previously generated production rules are transferred to an expert system in order to deduce the potential accident risks. Finally, a case-based reasoning (CBR) system makes it possible to search, on the basis of “experience feedback”, for the cases closest to this new risk of accident and proposes the most appropriate prevention or protection measures.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Ganascia, J.-G.: Agape et Charade : deux mécanismes d’apprentissage symbolique appliqués à la construction de bases de connaissances. Thèse d'État, Université Paris-sud, France (1987)
Hadj-Mabrouk, H.: CLASCA: learning system for classification and capitalization of accident scenarios of railway. J. Eng. Res. Appl. 6(8), 91–98 (2016a)
Li, J., Wang, J., Xu, N., Hu, Y., Cui, C.: Importance degree research of safety risk management processes of urban rail transit based on text mining method. J. Inf. 9(2), 26 (2018)
Hayward, V.: Big Data and the Digital Railway (2018). Available from: https://on-trac.co.uk/big-data-digital-railway/
Williams, T., Betakbc, J.: A comparison of LSA and LDA for the analysis of railroad accident text. Procedia Comput. Sci. 130, 98–102 (2018)
Faghih-Roohi, S., Hajizadeh, S., Núñez, A., et al.: Deep convolutional neural networks for detection of rail surface defects. In: International Joint Conference on Neural Networks (IJCNN), July 2016, Canada, pp. 24–29 (2016)
Marr, B.: How Siemens is Using Big Data and IoT to Build the Internet of Trains (2017). Available from: https://www.forbes.com/sites/bernardmarr/2017/05/30/how-siemens-is-using-big-data-and-iot-to-build-the-internet-of-trains/#2b7a4b6e72b8
Shirazi, K.N., Ul Hassan, N., Naqvi, S.A.-A., Parkinson, H.J., Bamford, G.: Big data and natural language processing for analysing railway safety. In: Innovative Applications of Big Data in the Railway Industry, pp. 240–267. IGI Global Publishing (2017)
Ghomi, H., Bagheri, M., Fu, L., Miranda-Moreno, L.-F.: Analyzing injury severity factors at highway railway grade crossing accidents involving vulnerable road users: a comparative study. Traffic Inj. Prev. 17(8), 833–841 (2016)
Zhang, X., Green, E., Chen, M., Souleyrette, R.R.: Identifying secondary crashes using text mining techniques. J. Transp. Saf. Secur. (2019). https://doi.org/10.1080/19439962.2019.1597795
Heidarysafa, M., Kowsari, K., Barnes, L.-E., Brown, D.-E.: Analysis of railway accidents’ narratives using deep learning. In: International Conference on Machine Learning and Applications (IEEE ICMLA) (2018). arXiv: 1810.07382 [cs.CL]. https://doi.org/10.1109/ICMLA.2018.00235
Zubair, M., Khan, M.J., Awais, M.: Prediction and analysis of air incidents and accidents using case-based reasoning. In: Third Global Congress on Intelligent Systems, 6–8 Nov 2012, Wuhan, China (2012)
Khattak, A., Kanafani, A.: Case-based reasoning: a planning tool for intelligent transportation systems. Transport. Res. C—Emer. Technol. 4, 267–288 (1996)
Louati, A., Elkosantini, S., Darmoul, S., et al.: A case-based reasoning system to control traffic at signalized intersections. IFAC-PapersOnLine 49, 149–154 (2016)
Cui, Y., Tang, Z., Dai, H.: Case-based reasoning and rule-based reasoning for railway incidents prevention. In: Proceedings of ICSSSM ‘05. 2005 International Conference on Services Systems and Services Management, Chongquing, China, pp. 13–15 (2005)
Varma, A., Roddy, N.: ICARUS: design and deployment of a case-based reasoning system for locomotive diagnostics. Eng. Appl. Artif. Intel. 12, 681–690 (1999)
Zhao, H., Chen, H., Dong, W., Sun, X., Ji, Y.: Fault diagnosis of rail turnout system based on case-based reasoning with compound distance methods. In: 29th Chinese Control and Decision Conference (CCDC), pp. 4205–4210 (2017). https://doi.org/10.1109/CCDC.2017.7979237
Hadj-Mabrouk, H.: Preliminary hazard analysis (PHA): new hybrid approach to railway risk analysis. Int. Refereed J. Eng. Sci. 6(2), 51–58 (2017)
Hadj-Mabrouk, H.: Machine learning from experience feedback on accidents in transport. Published in 7th International Conference on Sciences of Electronics, Technologies of Information and Telecommunications, pp. 246–251(2016b). https://doi.org/10.1109/SETIT.2016.7939874
Quinlan, J.R.: Induction of decision trees. Mach. Learn. 1, 81–106 (1986)
Hadj-Mabrouk, H.: Contribution of learning Charade system of rules for the prevention of rail accidents. Intell. Decis. Technol. 11, 477–485 (2017)
Hadj-Mabrouk, H.: A hybrid approach for the prevention of railway accidents based on artificial intelligence. In: Vasant, P., Zelinka, I., Weber, G.W. (eds.) International Conference on Intelligent Computing and Optimization, pp. 383–394 (2018a)
Hadj-Mabrouk, H.: New approach of assessing human errors in railways. Trans. VSB—Tech. Univ. Ostrava, Saf. Eng. Ser. 13(2), 1–17 (2018b)
Hadj-Mabrouk, H.: Contribution of artificial intelligence to risk assessment of railway accidents. J. Urban Rail Transit 5(2), 104–122 (2019a)
Hadj-Mabrouk, H.: Contribution of artificial intelligence and machine learning to the assessment of the safety of critical software used in railway transport. J. AIMS Electron. Electr. Eng. 3(1), 33–70 (2019b)
Hadj-Mabrouk, H.: Contribution of machine learning to rail transport safety. Chapter 10. In: Vasant, P., et al. (eds.) Advances of Machine Learning in Clean Energy and the Transportation Industry, pp. 277–312. Nova Science Publishers. https://doi.org/10.52305/SJDR3905
Hadj-Mabrouk, H.: Decision support approach for assessing of rail transport: methods based on AI and machine learning, Chapter 5. In: Hassan, S., Mohamed, A. (eds.) Handbook of Research on Decision Sciences and Applications in the Transportation Sector, pp. 124–146. IGI Global (2021). https://doi.org/10.4018/978-1-7998-8040-0.ch005
Hadj-Mabrouk, H.: Application of case-based reasoning to the safety assessment of critical software used in rail transport . Saf. Sci. 131, 104928 (2020). https://doi.org/10.1016/j.ssci.2020.104928
Hadj-Mabrouk, H.: Case-based reasoning for safety assessment of critical software. Intell. Decis. Technol. 14(4), 463–479 (2020). https://doi.org/10.3233/IDT-200016
Shannon, C.E.: A mathematical theory of communication. Bell Syst. Tech. J. 27, 379–423 (1948)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this chapter
Cite this chapter
Habib, HM. (2023). Approach to Assist in the Discovery of Railway Accident Scenarios Based on Supervised Learning. In: Sharma, S.K., Upadhyay, R.K., Kumar, V., Valera, H. (eds) Transportation Energy and Dynamics. Energy, Environment, and Sustainability. Springer, Singapore. https://doi.org/10.1007/978-981-99-2150-8_7
Download citation
DOI: https://doi.org/10.1007/978-981-99-2150-8_7
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-2149-2
Online ISBN: 978-981-99-2150-8
eBook Packages: EngineeringEngineering (R0)