Contribution of Case Based Reasoning (CBR) in the Exploitation of Return of Experience. Application to Accident Scenarii in Railroad Transport

TL;DR

This paper presents a case-based reasoning (CBR) approach to analyze accident scenarios in rail transport, aiming to develop a knowledge-sharing tool that enhances safety and prevents accident recurrence using AI techniques.

Contribution

It introduces a novel CBR-based method for exploiting accident scenario data to support safety analysis and decision-making in rail transport systems.

Findings

Developed a feasibility model using CBR for accident analysis

Demonstrated the tool's potential to improve safety measures

Enhanced knowledge sharing among safety experts

Abstract

The study is from a base of accident scenarii in rail transport (feedback) in order to develop a tool to share build and sustain knowledge and safety and secondly to exploit the knowledge stored to prevent the reproduction of accidents / incidents. This tool should ultimately lead to the proposal of prevention and protection measures to minimize the risk level of a new transport system and thus to improve safety. The approach to achieving this goal largely depends on the use of artificial intelligence techniques and rarely the use of a method of automatic learning in order to develop a feasibility model of a software tool based on case based reasoning (CBR) to exploit stored knowledge in order to create know-how that can help stimulate domain experts in the task of analysis, evaluation and certification of a new system.