Machine learning from experience feedback on accidents in transport


== Abstract ==

International audience; In addition to identifying safety deficiencies evidenced by accidents, the independent investigative body makes recommendations to eliminate or reduce these deficiencies. Its main purpose is to advance transportation safety by conducting investigations of accidents in rail and other modes of transportation. He must answer several questions: what happened, why did it happen, and what can be done to reduce the risk of it happening again? In this process, one of the difficulties involved is finding abnormal accident scenarios which are capable of generating a specific hazard. This paper proposes an original method based on machine learning to assist investigators experts in their crucial task of analysis and assessment of the safety for railway transport systems in France. This contribution is based on the use of artificial intelligence techniques and has involved the development of several approaches and tools which assist in the modeling, storage and assessment of knowledge about safety. The proposed approach has two objectives, firstly to record and store experience concerning safety analyses, and secondly to assist those involved in the development and assessment of the systems in the demanding task of evaluating safety studies.


== Original document ==

The different versions of the original document can be found in:

* [https://hal.archives-ouvertes.fr/hal-02280980 https://hal.archives-ouvertes.fr/hal-02280980]

* [https://hal.archives-ouvertes.fr/hal-02424630 https://hal.archives-ouvertes.fr/hal-02424630]

* [http://ieeexplore.ieee.org/document/8012108 http://ieeexplore.ieee.org/document/8012108],
: [https://academic.microsoft.com/#/detail/2746812691 https://academic.microsoft.com/#/detail/2746812691]

* [http://xplorestaging.ieee.org/ielx7/7936636/7939824/07939874.pdf?arnumber=7939874 http://xplorestaging.ieee.org/ielx7/7936636/7939824/07939874.pdf?arnumber=7939874],
: [http://dx.doi.org/10.1109/setit.2016.7939874 http://dx.doi.org/10.1109/setit.2016.7939874]

* [http://xplorestaging.ieee.org/ielx7/8007434/8012058/08012108.pdf?arnumber=8012108 http://xplorestaging.ieee.org/ielx7/8007434/8012058/08012108.pdf?arnumber=8012108],
: [http://dx.doi.org/10.1109/dt.2017.8012108 http://dx.doi.org/10.1109/dt.2017.8012108]

* [https://ieeexplore.ieee.org/document/7939874 https://ieeexplore.ieee.org/document/7939874],
: [http://ieeexplore.ieee.org/document/7939874 http://ieeexplore.ieee.org/document/7939874],
: [https://academic.microsoft.com/#/detail/2623993413 https://academic.microsoft.com/#/detail/2623993413]



DOIS: 10.1109/setit.2016.7939874 10.1109/dt.2017.8012108