A content-aware corpus-based model for analysis of marine accidents

Kai Yan; Yanhui Wang; Limin Jia; Wenhao Wang; Shengli Liu; Yanbin Geng

doi:10.1016/j.aap.2023.106991

A content-aware corpus-based model for analysis of marine accidents

Accid Anal Prev. 2023 May:184:106991. doi: 10.1016/j.aap.2023.106991. Epub 2023 Feb 9.

Authors

Kai Yan¹, Yanhui Wang², Limin Jia³, Wenhao Wang⁴, Shengli Liu⁵, Yanbin Geng⁶

Affiliations

¹ State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing 100044, China; School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China; Transport Planning and Research Institute, Ministry of Transport, Beijing, 100028, China; Laboratory of Transport Safety and Emergency Technology, Beijing 100028, China.
² State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing 100044, China; School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China; Transport Planning and Research Institute, Ministry of Transport, Beijing, 100028, China; Beijing Research Center of Urban Traffic Information Sensing and Service Technology, Beijing Jiaotong University, Beijing 100044, China; Research and Development Center of Transport Industry of Technologies and Equipment of Urban Rail Operation Safety Management, Beijing 100044, China. Electronic address: wangyanhui@bjtu.edu.cn.
³ State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing 100044, China; School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China; Transport Planning and Research Institute, Ministry of Transport, Beijing, 100028, China; Beijing Research Center of Urban Traffic Information Sensing and Service Technology, Beijing Jiaotong University, Beijing 100044, China; Research and Development Center of Transport Industry of Technologies and Equipment of Urban Rail Operation Safety Management, Beijing 100044, China.
⁴ State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing 100044, China; School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China.
⁵ Transport Planning and Research Institute, Ministry of Transport, Beijing, 100028, China; Laboratory of Transport Safety and Emergency Technology, Beijing 100028, China.
⁶ Transport Planning and Research Institute, Ministry of Transport, Beijing, 100028, China.

PMID: 36773468
DOI: 10.1016/j.aap.2023.106991

Abstract

In the past decades, marine accidents brought the serious loss of life and property and environmental contamination. With the accumulation of marine accident data, especially accident investigation reports, compared with subjective reasoning based on expert experience, data-driven methods for analysis and accident prevention are more comprehensive and objective. This paper aims to develop a content-aware corpus-based model for the analysis of marine accidents to mine the accident semantic features. The general research framework is established to combine accident data, expert prior knowledge, and semi-automated natural language processing (NLP) technology. The NLP models are optimized, fused, and applied to the case study of ship collision accidents. The results show that the proposed model can accurately and quickly extract hazards, accident causes, and scenarios from the accident reports, and perform semantic analysis for the latent relationships between them to extend the accident causation theory. This study can provide a powerful and innovative analysis tool for marine accidents for maritime traffic safety management departments and relevant research institutions.

Keywords: Accident analysis; Hazard identification; Marine accident; Natural language processing; Topic model.

MeSH terms

Accident Prevention
Accidents*
Accidents, Traffic*
Causality
Humans
Safety Management