A hybrid human and organizational analysis method for railway accidents based on HFACS-Railway Accidents (HFACS-RAs). (January 2017)

• Record Type: Journal Article
• Authors: Zhan, Qingjian; Zheng, Wei; Zhao, Bobo
• Is Part Of: Safety science. Volume 91(2016)
• Page Start: 232
• Page End: 250
• Publication Date: 2017-01
• Subjects: Human factor -- HFACS-RAs -- ANP -- Fuzzy DEMATEL; Industrial accidents -- Periodicals; Accident Prevention -- Periodicals; Safety -- Periodicals; Travail -- Accidents -- Périodiques; 363.11
• DOI: 10.1016/j.ssci.2016.08.017
• Languages: English
• ISSNs: 0925-7535

Abstract

Highlights: The HFACS-Railway Accidents framework for the railway system in China was proposed. The organizational and supervision deficiencies and human errors were disclosed. The quantitative correlation analysis was conducted to identify the key causes. The dynamic process of the accident is presented based on the casual diagram. Abstract: Accidents continue to be the major concern in the railway industry, and human factors have been proved to be the prime causes to railway accidents. In this paper, the Human Factors Analysis and Classification System-Railway Accidents (HFACS-RAs) framework is proposed to identify and classify human and organizational factors involved in railway accidents. To establish an applicable HFACS-RAs framework, large amount of incident and accident data are collected and the existing safety flaws are identified by safety experts, manufactures and railway managers who have attended the HFACS workshop. To find out the leading accident casual factors, the Analytical Network Process (ANP) method combined with Fuzzy Decision Making Trail and Evaluation (DEMATEL) method is adopted to analyze the influence relationships of human and organization factors classified by HFACS-RAs framework after its reliability is demonstrated. The expert judgement is required in most phases in this study for the uncertainty and complexity of the human and organizational factors and the proposed method to identify the main casual factors is elaborated in the case study. The relevant preventative measures can be raised to avoid the recurrence of similar accidents after the investigation. Finally some considerations on further work are discussed.