Zeng Qiang, Wen Huiying, Huang Helai, Abdel-Aty Mohamed
School of Civil Engineering and Transportation, South China University of Technology, Guangzhou, Guangdong 510641, PR China.
Urban Transport Research Center, School of Traffic and Transportation Engineering, Central South University, Changsha, Hunan 410075, PR China.
Accid Anal Prev. 2017 Mar;100:37-43. doi: 10.1016/j.aap.2016.12.023. Epub 2017 Jan 11.
This study develops a Bayesian spatial random parameters Tobit model to analyze crash rates on road segments, in which both spatial correlation between adjacent sites and unobserved heterogeneity across observations are accounted for. The crash-rate data for a three-year period on road segments within a road network in Florida, are collected to compare the performance of the proposed model with that of a (fixed parameters) Tobit model and a spatial (fixed parameters) Tobit model in the Bayesian context. Significant spatial effect is found in both spatial models and the results of Deviance Information Criteria (DIC) show that the inclusion of spatial correlation in the Tobit regression considerably improves model fit, which indicates the reasonableness of considering cross-segment spatial correlation. The spatial random parameters Tobit regression has lower DIC value than does the spatial Tobit regression, suggesting that accommodating the unobserved heterogeneity is able to further improve model fit when the spatial correlation has been considered. Moreover, the random parameters Tobit model provides a more comprehensive understanding of the effect of speed limit on crash rates than does its fixed parameters counterpart, which suggests that it could be considered as a good alternative for crash rate analysis.
本研究开发了一种贝叶斯空间随机参数Tobit模型，用于分析路段的撞车率，该模型考虑了相邻地点之间的空间相关性以及观测值之间未观测到的异质性。收集了佛罗里达州道路网络中路三年期间的路段撞车率数据，以在贝叶斯背景下比较所提出模型与（固定参数）Tobit模型和空间（固定参数）Tobit模型的性能。在两个空间模型中均发现了显著的空间效应，并且偏差信息准则（DIC）的结果表明，在Tobit回归中纳入空间相关性可显著改善模型拟合，这表明考虑跨路段空间相关性是合理的。空间随机参数Tobit回归的DIC值低于空间Tobit回归，这表明在考虑空间相关性时，纳入未观测到的异质性能够进一步改善模型拟合。此外，与固定参数的Tobit模型相比，随机参数Tobit模型能更全面地理解限速对撞车率的影响，这表明它可被视为撞车率分析的一个良好替代模型。
