A multi-level analysis on the causes of train-pedestrian collisions in Southwest China 2011-2020

Accid Anal Prev. 2023 Dec:193:107332. doi: 10.1016/j.aap.2023.107332. Epub 2023 Oct 4.

Abstract

Collisions between trains and pedestrians are the primary cause of railway casualties. However, there remains a lack of comprehensive understanding regarding the underlying causes of this phenomenon. This study employs a multi-level approach to investigate the factors associated with the occurrence and severity of train-pedestrian collisions. The investigation is based on 2160 independent cases that occurred in southwest China from 2011 to 2020. Multiple contributing factors related to the victim, train, track, and socio-economic status of the surrounding district were examined, utilizing information from various sources. At the county level, several risk factors were identified in predicting the occurrence rate. These factors include higher population density and a greater number of normal-speed stations. However, the presence of high-speed train stations did not exhibit any significant impact. Additionally, the study found that regulations pertaining to protective fences were highly effective in reducing the occurrence rate. Regarding the prediction of collision severity, certain factors were found to increase the death rate. These factors include young men as victims, engaging in lying down or crossing behaviors, higher train speeds, gentle downhill slopes, lower education levels, and a higher proportion of the labor force. These findings emphasize the necessity of adopting a comprehensive perspective when examining the causes of train-pedestrian collisions. Furthermore, it underscores the significance of considering the notable differences between rapidly developing countries such as China and developed countries. Based on our findings, we also provide corresponding policy suggestions.

Keywords: Mixed-effects models; Multi-level analysis; Railway collision; Train-pedestrian collision.

MeSH terms

  • Accidents, Traffic*
  • Causality
  • China / epidemiology
  • Humans
  • Male
  • Pedestrians*
  • Risk Factors
  • Walking