Injury severity analysis of highway-rail grade crossing crashes in non-divided two-way traffic scenarios: A random parameters logit model

Ren, Qiaoqiao; Xu, Min

doi:10.1016/j.multra.2023.100109

Cited by 10 publications

(2 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Lastly, truck being a common feature underscores the important role of vehicle type in HRGC accidents. It might be that the proficiency of truck drivers, who generally follow safety regulations and are careful at rail crossings, leads to the reduced severity of incidents observed [9]. Furthermore, trucks and other larger cars may offer their occupants superior protection, reducing the risk of injuries or fatalities at crossings.…”

Section: Resultsmentioning

confidence: 99%

“…For example, Ref. [9] investigated the HRGC crashes in the United States by employing a random parameters logit model with heterogeneity in means to account for varied factors influencing crashes. It highlighted specific behaviors and driver attributes, such as non-compliance with stop signals, old driver, and female driver, would raise the possibility of severe injuries.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Traffic Safety Assessment and Injury Severity Analysis for Undivided Two-Way Highway–Rail Grade Crossings

Ren,

Xu,

Zhou

et al. 2024

Mathematics

Self Cite

View full text Add to dashboard Cite

The safety and reliability of undivided two-way highway–rail grade crossings (HRGCs) are of paramount importance in transportation systems. Utilizing crash data from the Federal Railroad Administration between 2020 and 2021, this study aims to predict crash injury severity outcomes and investigate various factors influencing injury severities. The χ2 test was first used to select variables that were significantly associated with injury outcomes. By employing the eXtreme Gradient Boosting (XGBoost) model and interpretable SHapley Additive exPlanations (SHAP), a cross-category safety assessment that offers an evidence-based hierarchy and statistical inference of risk factors associated with crashes, crossings, vehicles, drivers, and environment was provided for killed, injured, and uninjured outcomes. Some significant predictors overlapped between the killed and injured models, such as old driver, driver was in vehicle, main track, went around the gate, adverse crossing surface, and truck, while the other different significant factors revealed that the model could distinguish between different severity levels. Additionally, the results suggested that the model has varying performances in predicting different injury severities, with the killed model having the highest accuracy of 93.36%. The SHAP dependency plots for the top three features also ensure reliable predictions and inform potential interventions aimed at strengthening traffic safety and risk management practices, such as enhanced warning systems and targeted educational campaigns for older drivers.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%