分析未观察到异质性的单车和多车高速公路碰撞

IF 2.4 3区 工程技术 Q3 TRANSPORTATION Journal of Transportation Safety & Security Pub Date : 2021-12-29 DOI:10.1080/19439962.2021.2020945
Mingjie Feng, Xuesong Wang, Yan Li
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引用次数: 5

摘要

近年来,中国高速公路发展迅速。然而,庞大的交通量和高速行驶造成了一个日益受到关注的严重安全问题。准确识别影响碰撞的因素是实施对策的先决条件,但碰撞数据中未观察到的异质性可能导致错误的推断。为了确定影响碰撞发生的关键因素,本研究使用了两种数据准备和建模方法来解释未观察到的异质性。首先,高速公路交通事故分为单车(SV)和多车(MV)崩溃,因为他们不同的发生机制。其次,使用随机参数建模和有限混合建模,并就其解释未观察到的异质性的能力进行比较。结果表明,两组分有限混合负二项回归模型(FMNB-2)具有较好的拟合优度和参数估计。结果FMNB-2 SV和MV模型的事故分类为两个同构子组表明,SV和MV,崩溃在组件1被道路几何特性影响最大,而在组件2,崩溃更与交通运营条件密切相关。这些发现将有助于交通管理者实施更有针对性的高速公路安全改善对策。
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Analyzing single-vehicle and multi-vehicle freeway crashes with unobserved heterogeneity
Abstract Freeways in China have developed rapidly in recent years. The large traffic volumes and high travel speeds have created a serious safety problem that is of growing concern, however. Accurate identification of factors influencing crashes is a prerequisite for implementing countermeasures, but unobserved heterogeneity in crash data can lead to erroneous inferences. To identify key factors influencing crash occurrence, this study used two data preparation and modeling approaches to account for unobserved heterogeneity. First, freeway traffic crashes were divided into single-vehicle (SV) and multi-vehicle (MV) crashes because of their different mechanisms of occurrence. Second, random parameter modeling and finite mixture modeling were used, and were compared with regard to their ability to account for unobserved heterogeneity. The results indicated that the finite mixture negative binomial regression model with two components (FMNB-2) produced a better goodness-of-fit and parameter estimation. Results of the FMNB-2 SV and MV models’ classification of crashes into two homogeneous subgroups showed that for both SV and MV, crashes in Component 1 were most affected by roadway geometric features, while in Component 2, crashes were more strongly associated with traffic operational conditions. These findings will help traffic managers implement more targeted countermeasures for freeway safety improvement.
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来源期刊
CiteScore
6.00
自引率
15.40%
发文量
38
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