A Random Parameters Frequency Model for Highway Crash Analysis Considering Spatial Spillover Effects
To enhance the accuracy of parameter estimation for highway crash frequency models,this paper utilizes crash records,road attributes,traffic flow conditions,and weather condition data as samples to compares the fitting performances of several conventional crash frequency models.The Poisson-lognormal distribution model,which exhibits the best performance,is chosen as the foundational model for optimization.Then,the paper considers the spatial spillover effects of adjacent road segments and investigates additional spatial effects influencing crash frequency on highways.A model with spatial spillover covariates is developed to analyze the impact of spatial spillover effects on crash frequency of road segments in consideration of conditional autoregressive priors.Additionally,a random parameter model is developed to capture the influence of data heterogeneity on crash frequency of road segments.The results demonstrate the effectiveness of spatial spillover effects,and the goodness-of-fit of the proposed models have been improved compared to the control model.Based on the parameter estimation results of the optimal model,risk factors are identified,including"ln(MADT)","ln(road length)","category 1 vehicles","category 4 vehicles",and"precipitation"as ordinary variables,as well as spatial spillover covariates such as"category 1 vehiclesS"and"curvature lengthS",which exhibit significant correlations with crash frequency.
traffic engineeringspatial spillover effectcrash frequency modelhighwayrandom parametersspatial correlation
NETL
NSTL
万方数据
维普
