Enhancing bicyclist survival time in fatal crashes: Investigating the impact of faster crash notification time through explainable machine learning

AbstractBicyclists are recognized as vulnerable road users, with the escalating fatalities posing a safety concern. While fatal crashes involving bicyclists are often assumed to be similar, there is a crucial distinction between instant death and death occurring several days later, with the former being substantially more severe. This study delves into the analysis of bicyclists’ time-to-death, spanning from immediate fatalities to deaths within 30 days, using data from the Fatality Analysis Reporting System from 2015 to 2019. Employing the Haddon Matrix approach, the variables are categorized into pre-crash, during-crash, and postcrash phases. This study considers crash notification time as the key postcrash measure. An explainable XGBoost model is developed using the SHAP technique to investigate the associations between variables and bicyclist time-to-death. The results show that substantial delays in crash notification time considerably reduce bicyclists’ time-to-death and increase the likelihood of early death. Specifically, hit-and-run crashes, crashes in rural areas, and crashes during late-night hours exhibit notably longer crash notification times compared to non-hit-and-run crashes, urban areas, and other hours, respectively. In such cases, when no witnesses or survivors can notify emergency responders, on-road vehicle technologies like the advanced automatic collision notification system can promptly inform responders, reducing notification delays.Keywords: bicyclist time-to-deathcrash notification timeexplainable machine learningXGBoostSHAP value AcknowledgementsThe authors express their gratitude for the financial support that enabled the research, authorship, and publication of this article. Specifically, this project received partial funding from the Tennessee Department of Transportation and the US Department of Transportation through the Collaborative Sciences Center for Road Safety (Grant No. 69A3551747113). It is important to note that the views presented in this paper are solely those of the authors, who bear responsibility for the content of this publication.Disclosure statementThe authors report no declarations of interest.Data availability statementThe data that support the findings of this study are openly available in FARS at https://www.nhtsa.gov/research-data/fatality-analysis-reporting-system-fars.