Eye movement predictors of hazard response performance in train drivers under different speed conditions

Foreign objects on railroads contribute to railway accidents. Train driving heavily relies on visual prowess. Drivers must quickly identify and react to these hazards, capabilities that directly impact the safety and efficiency of train operations. As train speeds increase, it becomes increasingly challenging to spot track anomalies. This underscores the importance of studying drivers' visual attention, especially under different speed conditions. This study delved into the nuances of train drivers' hazard detection by analyzing eye movement and response performance to simulated foreign object encroachments at different speeds. Our objective was to identify eye movement indicators predictive of train drivers' hazard response efficacy and to understand how these vary across different speed scenarios. The analysis highlighted distinct predictors for effective hazard response at different speeds. At standard speeds, maintaining consistent attention is crucial. The time spent searching positively correlates with hazard detection efficiency in these conditions. At higher speeds, rapid information processing becomes key, and a shorter average gaze duration indicates better hazard response. The study also revealed that train drivers tend to exhibit a left-leaning attention bias when recognizing ground-level foreign object encroachments. These insights support the development of improved training programs for train drivers, optimization of track and signal marker placement, and design of effective foreign object detection systems.

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Publication Details

Journal
Scientific Reports
Published
2025-07-05
DOI
https://doi.org/10.1038/s41598-025-09008-9
Citations
2
Primary Topic
Human-Automation Interaction and Safety
Type
article
Field-Weighted Citation Impact
4.88
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article

Eye movement predictors of hazard response performance in train drivers under different speed conditions

Maosheng Xia, Fang Ding, Lu Yang, Xu Jing et al.
2 citations
Scientific Reports
Human-Automation Interaction and Safety
4.88
article

Eye movement predictors of hazard response performance in train drivers under different speed conditions

Maosheng Xia, Fang Ding, Lu Yang, Xu Jing, Yusong Wang, Jinfei Ma
article en
2 citations

Abstract

Foreign objects on railroads contribute to railway accidents. Train driving heavily relies on visual prowess. Drivers must quickly identify and react to these hazards, capabilities that directly impact the safety and efficiency of train operations. As train speeds increase, it becomes increasingly challenging to spot track anomalies. This underscores the importance of studying drivers' visual attention, especially under different speed conditions. This study delved into the nuances of train drivers' hazard detection by analyzing eye movement and response performance to simulated foreign object encroachments at different speeds. Our objective was to identify eye movement indicators predictive of train drivers' hazard response efficacy and to understand how these vary across different speed scenarios. The analysis highlighted distinct predictors for effective hazard response at different speeds. At standard speeds, maintaining consistent attention is crucial. The time spent searching positively correlates with hazard detection efficiency in these conditions. At higher speeds, rapid information processing becomes key, and a shorter average gaze duration indicates better hazard response. The study also revealed that train drivers tend to exhibit a left-leaning attention bias when recognizing ground-level foreign object encroachments. These insights support the development of improved training programs for train drivers, optimization of track and signal marker placement, and design of effective foreign object detection systems.

Scientific ReportsVol. 15(1)
Liaoning Normal University (CN), China Railway Corporation (CN), China Railway Construction Corporation (China) (CN), China Railway Group (China) (CN), Southwest Jiaotong University (CN)
Climate action
Openalex Percentile: Top 5%
Human-Automation Interaction and Safety
4.88
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