Modeling correlation and heterogeneity in crash rates by collision types using full bayesian random parameters multivariate Tobit model
Modeling correlation and heterogeneity in crash rates by collision types using full bayesian random parameters multivariate Tobit model
复制标题
使用完整贝叶斯随机参数多元 Tobit 模型对碰撞类型的碰撞率相关性和异质性进行建模
DOI：
10.1016/j.aap.2019.04.013
复制
发表时间：
2019-07-01
影响因子：
5.9
通讯作者：
Wu, Yao
中科院分区：
文献类型：
--
作者：
Guo, Yanyong;Li, Zhibin;Wu, Yao
崩溃呈现不同的碰撞类型。通常存在未观察到的风险因素，这些因素可能共同影响不同类型的碰撞率，从而导致观察结果之间的相关性和异质性问题。研究的主要目的是提出一种新的随机参数多元Tobit （RPMV-Tobit）模型来评估不同碰撞类型的碰撞率风险因素。从367个高速公路岔道区获得了三年的碰撞数据进行建模。考虑了三种主要的碰撞类型，包括追尾碰撞、侧击碰撞和角度碰撞。构建了RPMV-Tobit模型，以同时适应不同碰撞类型的碰撞率之间的相关性和观察结果中未观察到的异质性。将RPMV-Tobit模型与贝叶斯框架下的多元Tobit （MV-Tobit）模型、随机效应多元Tobit （REMV-Tobit）模型和独立单变量Tobit （IU-Tobit）模型进行比较。结果表明，MV-Tobit模型在拟合碰撞率方面优于IU-Tobit模型，说明考虑碰撞类型之间的相关性可以改善模型的拟合。RPMV-Tobit模型和REMV-Tobit模型的拟合效果优于MV-Tobit模型，说明考虑未观察到的异质性可以进一步提高模型的拟合效果。RPMV-Tobit模型对模型性能的改善高于REMV-Tobit模型。估计了每个风险因素对碰撞率的影响，并在不同的碰撞类型中发现了一些差异。车道平衡设计、干线车道数、速度限制和速度差对碰撞率有显著的异质性影响。研究结果表明，RPMV-Tobit模型是综合碰撞率建模和交通安全评价的优越方法。
Crashes present different collision types. There usually exist unobserved risk factors which could jointly affect crash rates of different types, resulting in correlation and heterogeneity issues across observations. The primary objective of the study is to propose a novel random parameters multivariate Tobit (RPMV-Tobit) model for evaluating risk factors on crash rates of different collision types. Crash data from 367 freeway diverge areas in a three-year period were obtained for modeling. Three major types of collisions including rear-end, sideswipe, and angle collisions were considered. The RPMV-Tobit model was structured to simultaneously accommodate correlations between crash rates across collision types and unobserved heterogeneity across observations. The RPMV-Tobit model was compared with a multivariate Tobit (MV-Tobit) model, a random effect multivariate Tobit (REMV-Tobit) model, and independent univariate Tobit (IU-Tobit) models under the Bayesian framework. The results showed that MV-Tobit model outperforms the IU-Tobit models on fitting crash rates, indicating that accounting for the correlation between crash types can improve model fit. The RPMV-Tobit model and REMV-Tobit model perform better than the MV-Tobit model, suggesting that accounting for the unobserved heterogeneous can further improve model fit. The improvement of model performance with the RPMV-Tobit model is higher than that with the REMV-Tobit model. The impacts of each risk factor on crash rates were estimated and some differences were found across different collision types. The lane-balanced design, number of lanes on mainline, speed limit, and speed difference present significant heterogeneous effects on crash rates. Findings suggest that the RPMV-Tobit model is a superior approach for comprehensive crash rates modeling and traffic safety evaluation purposes.