建成环境对交通事故伤亡的时变影响OA

[Background]With the acceleration of urbanization and continuous growth of motor vehi-cle ownership,traffic accidents have become a critical public issue constraining social safety devel-opment in China.Traditional studies frequently use univariate models that focus solely on fatalities or injuries,limiting our understanding of their joint mechanisms.In addition,existing research pro-vides insufficient evidence regarding the temporal variability of influencing factors.[Method]This study developed a bivariate negative binomial regression model based on the Gaussian Copula frame-work to examine the joint distribution of traffic fatalities and injuries across prefecture-level cities in the Beijing-Tianjin-Hebei region.The model also evaluates the temporal stability of the impact of built environment characteristics on crash casualties across multiple time periods.[Data]The study used crash records from the Beijing-Tianjin-Hebei region from 2017 to 2020,as well as socioeco-nomic indicators and point of interest(POI)data.The built-environment variables were constructed from eight dimensions:economic development,demographic attributes,living services,consumption and entertainment facilities,business establishments,scenic attractions,public services,and transpor-tation facilities.[Result]The results revealed significant positive correlations between fatalities and injuries across all years,with correlation coefficients of 0.644,0.610,0.527,and 0.503(all P<0.001).The optimal Gaussian copula-based bivariate negative binomial models indicate that the tem-porally stable factors influencing fatalities include Gross Domestic Product(GDP),population densi-ty,and scenic attractions,among others,with population density showing the strongest effect in 2020(-0.930;P<0.001).The stable factors for injuries included population density,automobile services,and catering services,with the latter showing the highest impact in 2019(1.634;P<0.001).[Appli-cation]This study extends the theoretical framework for modeling crash data and provides empirical insights to support traffic safety management and urban planning in the Beijing-Tianjin-Hebei re-gion,particularly for mitigating human-vehicle conflicts,optimizing dynamic traffic control strate-gies,and improving transportation infrastructure.