Escalator accident mechanism analysis and injury prediction approaches in heavy capacity metro rail transit stations

Zhiru Wang, Yu Pang, Mingxin Gan, Martin Skitmore, Feng Li

Research output: Contribution to journalArticleResearchpeer-review

Abstract

The semi-open character with high passenger flow in Metro Rail Transport Stations (MRTS) makes safety management of human-electromechanical interaction escalator systems more complex. Safety management should not consider only single failures, but also the complex interactions in the system. This study applies task driven behavior theory and system theory to reveal a generic framework of the MRTS escalator accident mechanism and uses Lasso-Logistic Regression (LLR) for escalator injury prediction. Escalator accidents in the Beijing MRTS are used as a case study to estimate the applicability of the methodologies. The main results affirm that the application of System-Theoretical Process Analysis (STPA) and Task Driven Accident Process Analysis (TDAPA) to the generic escalator accident mechanism reveals non-failure state task driven passenger behaviors and constraints on safety that are not addressed in previous studies. The results also confirm that LLR is able to predict escalator accidents where there is a relatively large number of variables with limited observations. Additionally, increasing the amount of data improves the prediction accuracy for all three types of injuries in the case study, suggesting the LLR model has good extrapolation ability. The results can be applied in MRTS as instruments for both escalator accident investigation and accident prevention.
Original languageEnglish
Article number105850
JournalSafety Science
Volume154
Early online date28 Jun 2022
DOIs
Publication statusE-pub ahead of print - 28 Jun 2022

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