Howlader Md Mohasin, Yasmin Shamsunnahar, Bhaskar Ashish, Haque Md Mazharul
Queensland University of Technology, School of Civil and Environmental Engineering, Brisbane, Australia.
Queensland University of Technology, Centre for Accident Research and Road Safety-Queensland (CARRS-Q), Brisbane, Australia.
Accid Anal Prev. 2023 Jan;179:106882. doi: 10.1016/j.aap.2022.106882. Epub 2022 Nov 7.
Right-turn crashes (or left-turn crashes for the US or similar countries) represent over 40 % of signalized intersection crashes in Queensland, Australia. Protected right-turn phasings are a widely used countermeasure for right-turn crashes, but the research findings on their effects across different crash types and intersection types are not consistent. Methodologically, the Empirical Bayes and Full Bayes techniques are generally applied for before-after evaluations, but the inclusion of heterogeneous models within these techniques has not been considered much. Addressing these research gaps, the objective of this study is to evaluate the effectiveness of protected right-turn signal phasings at signalized intersections employing heterogeneous count data models with the Empirical Bayes and Full Bayes techniques. In particular, the Empirical Bayes approach based on random parameters Poisson-Gamma models (simulation-based Empirical Bayes), and the Full Bayes approach based on random parameters Poisson-Lognormal intervention models (simulation-based Full Bayes) are applied. A total of 69 Cross intersections (with ten treated sites) and 47 T intersections (with six treated sites) from Southeast Queensland in Australia were included in the analysis to estimate the effects of protected right-turn signal phasings on various crash types. Results show that the change of signal phasing from a permissive right-turn phasing to the protected right-turn phasing at cross and T intersections reduces about 87 % and 91 % of right-turn crashes, respectively. In addition, the effect of protected right-turn phasings on rear-end crashes was not significant. The heterogenous count data models significantly address extra Poisson variation, leading to efficient safety estimates in both simulation-based Empirical Bayes and simulation-based Full Bayes approaches. This study demonstrates the importance of accounting for unobserved heterogeneity for the before-after evaluation of engineering countermeasures.
在澳大利亚昆士兰州，右转撞车事故（在美国或类似国家则为左转撞车事故）占信号控制交叉口撞车事故的40%以上。受保护的右转相位是一种广泛用于减少右转撞车事故的对策，但关于其对不同撞车类型和交叉口类型影响的研究结果并不一致。在方法上，经验贝叶斯和全贝叶斯技术通常用于前后评估，但这些技术中异质性模型的纳入尚未得到充分考虑。为填补这些研究空白，本研究的目的是采用经验贝叶斯和全贝叶斯技术，利用异质性计数数据模型评估信号控制交叉口受保护右转信号相位的有效性。具体而言，应用了基于随机参数泊松 - 伽马模型的经验贝叶斯方法（基于模拟的经验贝叶斯）和基于随机参数泊松 - 对数正态干预模型的全贝叶斯方法（基于模拟的全贝叶斯）。分析纳入了澳大利亚昆士兰州东南部的69个十字形交叉口（其中10个为处理站点）和47个T形交叉口（其中6个为处理站点），以估计受保护右转信号相位对各种撞车类型的影响。结果表明，在十字形和T形交叉口，信号相位从允许右转相位变为受保护右转相位后，右转撞车事故分别减少了约87%和91%。此外，受保护右转相位对追尾撞车事故的影响不显著。异质性计数数据模型显著解决了泊松额外变异问题，在基于模拟的经验贝叶斯和基于模拟的全贝叶斯方法中都能实现有效的安全估计。本研究证明了在工程对策前后评估中考虑未观察到的异质性的重要性。
