Conditional autoregressive negative binomial model for analysis of crash count using Bayesian methods

用于交通事故分析的基于贝叶斯方法的条件自回归负二项模型(英文)

In order to improve crash occurrence models to account for the influence of various contributing factors, a conditional autoregressive negative binomial （CAR-NB） model is employed to allow for overdispersion （tackled by the NB component）, unobserved heterogeneity and spatial autocorrelation （captured by the CAR process）, using Markov chain Monte Carlo methods and the Gibbs sampler. Statistical tests suggest that the CAR-NB model is preferred over the CAR-Poisson, NB, zero-inflated Poisson, zero-inflated NB models, due to its lower prediction errors and more robust parameter inference. The study results show that crash frequency and fatalities are positively associated with the number of lanes, curve length, annual average daily traffic （AADT） per lane, as well as rainfall. Speed limit and the distances to the nearest hospitals have negative associations with segment-based crash counts but positive associations with fatality counts, presumably as a result of worsened collision impacts at higher speed and time loss during transporting crash victims.

为了改进用于分析大量影响因素的交通事故模型,采用基于马尔可夫链蒙特卡罗法和吉布斯抽样的条件自回归负二项模型来拟合过度散布性(由负二项过程拟合)、未观察异质性和空间相关性(由条件自回归过程拟合).统计检验显示,由于具有更小的预测误差和更强的参数估计,条件自回归负二项模型优于条件自回归泊松模型、负二项模型、零膨胀泊松模型和零膨胀负二项模型.研究结果表明,交通事故率和死亡人数与车道数、曲线长度、车道年平均日交通量和降雨量成正比.最大限速和最近医院距离与交通事故次数成反比,而与死亡事故次数成正比,这可能是由于过高的速度会引发更严重的事故以及救援伤者时丧失较长时间.