Contribution of Artificial Intelligence to Risk Assessment of Railway Accidents

Abstract

In the design, development, and operation of a rail transport system, all the actors involved use one or more safety methods to identify hazardous situations, the causes of hazards, potential accidents, and the severity of the consequences that would result. The main objective is to justify and ensure that the design architecture of the transportation system is safe and presents no particular risk to users or the environment. As part of this process of certification, domain experts are responsible for reviewing the safety of the system, and are being brought in to imagine new scenarios of potential accidents to ensure the exhaustiveness of such safety studies. One of the difficulties in this process is to determine abnormal scenarios that could lead to a particular potential accident. This is the fundamental point that motivated the present work, whose objective is to develop tools to assist certification experts in their crucial task of analyzing and evaluating railway safety. However, the type of reasoning (inductive, deductive, by analogy, etc.) used by certification experts as well as the very nature of the knowledge manipulated in this certification process (symbolic, subjective, evolutionary, empirical, etc.) justify that conventional computer solutions cannot be adopted; the use of artificial intelligence (AI) methods and techniques helps to understand the problem of safety analysis and certification of high-risk systems such as guided rail transport systems. To help experts in this complex process of evaluating safety studies, we decided to use AI techniques and in particular machine learning to systematize, streamline, and strengthen conventional approaches used for safety analysis and certification.

Keywords

Rail safety / Risk assessment / Accident scenarios / Artificial intelligence / Machine learning / Expert system

References

[1] CENELEC—EN 50129 (2003) Railway applications—communication, signaling and processing systems—safety related electronic systems for signaling

[2] Hadj-Mabrouk H. Preliminary hazard analysis (PHA): new hybrid approach to railway risk analysis. Int Refereed J Eng Sci, 2017, 6(2): 51-58.

[3] Hadj-Mabrouk H. Contribution of artificial intelligence and machine learning to the assessment of the safety of critical software used in railway transport. AIMS Electron Electr Eng, 2019, 3(1): 33-70

[4] Villemeur A (1988) Sûreté de fonctionnement des systèmes industriels, Paris, Eyrolles, coll.  «Collection de la direction des études et recherches d’Électricité de France», juillet 1988, ISSN: 0399-4198

[5] Hadj-Mabrouk H (2016) Machine learning from experience feedback on accidents in transport. In: 7th International conference on sciences of electronics, technologies of information and telecommunications, 18–20 Dec. 2016, https://doi.org/10.1109/setit.2016.7939874, pp 246–251, http://ieeexplore.ieee.org/document/7939874/

[6] Hadj-Mabrouk H. Contribution of learning Charade system of rules for the prevention of rail accidents. J Intell Decis Technol, 2017, 11(4): 477-485

[7] Hadj-Mabrouk Habib. A Hybrid Approach for the Prevention of Railway Accidents Based on Artificial Intelligence. Intelligent Computing & Optimization, 2018, Cham: Springer International Publishing 383-394.

[8] Aussenac G, Gandon F. From the knowledge acquisition bottleneck to the knowledge acquisition overflow: a brief French history of knowledge acquisition. Int J Hum Comput Stud, 2013, 71(2): 157-165

[9] Gaines BR. Knowledge acquisition: past, present, and future. Int J Hum Comput Stud, 2012, 71(2): 135-156

[10] Dieng R (1990) Méthodes et outils d’acquisition des connaissances, ERGO IA90, Biarritz, France, 19 à 21 septembre

[11] Kodratoff Y (1986) Leçons d’apprentissage symbolique automatique. Cepadues éd, Toulouse, France

[12] Ganascia J-G (1987) Agape et Charade: deux mécanismes d’apprentissage symbolique appliqués à la construction de bases de connaissances. Thèse d’État, Université Paris-sud, France

[13] Ganascia J-G (2011) Logical induction, machine learning and human creativity. In: Switching codes. University of Chicago Press, ISBN 978022603830

[14] Michalski R-S, Wojtusiak J. Reasoning with missing, not-applicable and irrelevant meta-values in concept learning and pattern discovery. J Intell Inf Syst, 2012, 39(1): 141-166

[15] Jamal Salma, Goyal Sukriti, Grover Abhinav, Shanker Asheesh. Machine Learning: What, Why, and How?. Bioinformatics: Sequences, Structures, Phylogeny, 2018, Singapore: Springer Singapore 359-374

[16] Bergmeir Christoph, Sáinz Gregorio, Martínez Bertrand Carlos, Benítez José Manuel. A Study on the Use of Machine Learning Methods for Incidence Prediction in High-Speed Train Tracks. Recent Trends in Applied Artificial Intelligence, 2013, Berlin, Heidelberg: Springer Berlin Heidelberg 674-683

[17] Fay A. A fuzzy knowledge-based system for railway traffic control. Eng Appl Artif Intell, 2000, 13(6): 719-729

[18] Santur Y, Karaköse M, Akin E (2017) A new rail inspection method based on deep learning using laser cameras. In: International artificial intelligence and data processing symposium (IDAP). https://doi.org/10.1109/idap.2017.8090245

[19] Faghih-Roohi S, Hajizadeh S, Núñez A, Babuska R, De Schutter B (2016) Deep convolutional neural networks for detection of rail surface defects. In: International joint conference on neural networks (IJCNN), 24–29 July 2016, Canada. https://doi.org/10.1109/ijcnn.2016.7727522

[20] Ghofrania F, He Q, Goverde R, Liud X. Recent applications of big data analytics in railway transportation systems: a survey. Transp Res C Emerg Technol, 2018, 90: 226-246

[21] Thaduri A, Galar D, Kumar U. Railway assets: a potential domain for big data analytics. Procedia Comput Sci, 2015, 53: 457-467

[22] Nii Attoh-Okine (2014) Big data challenges in railway engineering. In: IEEE international conference on big data (big data), Washington, DC, USA. https://doi.org/10.1109/bigdata.2014.7004424

[23] Hughes P (2018) Making the railway safer with big data, 30.01.18. http://www.railtechnologymagazine.com/Comment/making-the-railway-safer-with-big-data

[24] Hayward V (2018) Big data & the digital railway. https://on-trac.co.uk/big-data-digital-railway

[25] Marr B (2017) How Siemens is using big data and IoT to build the internet of trains, May 30, 2017. https://www.forbes.com/sites/bernardmarr/2017/05/30/how-siemens-is-using-big-data-and-iot-to-build-the-internet-of-trains/#2b7a4b6e72b8

[26] Quinlan JR. Induction of decision trees. Mach Learn, 1986, 1: 81-106.

[27] Hadj-Mabrouk H. New approach of assessing human errors in railways. Trans VSB Tech Univ Ostrava Saf Eng Ser, 2018, 13(2): 1-17