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Hierarchical Bayesian modeling to evaluate the impacts of intelligent speed adaptation considering individuals’ usual speeding tendencies: A correlated random parameters approach
Analytic Methods in Accident Research ( IF 12.5 ) Pub Date : 2020-02-25 , DOI: 10.1016/j.amar.2020.100125
Kojiro Matsuo , Mitsuru Sugihara , Motohiro Yamazaki , Yasuhiro Mimura , Jia Yang , Komei Kanno , Nao Sugiki

Although there have been a large number of short- and long-term field trials and driving simulator-based studies on intelligent speed adaptation (ISA), there are a limited number of methods for evaluating the impact of ISA. In particular, appropriately evaluating the impacts of ISA through field trials requires the consideration of a number of factors related to detailed road characteristics. In addition, because of the “target speed” features of ISA itself, it is necessary to consider the usual speeding tendencies of individual drivers to avoid underestimating the impact of the ISA. In this study, a hierarchical Bayesian model with correlated random effects was developed to examine the impact of ISA on driver speed based on the driver’s typical speeding tendency and road characteristic conditions as confounding factors. The model was applied to clarify and compare the impacts of informative and incentive ISAs on community streets with a 30 km/h speed limit (“Zone 30”) based on data collected in a field trial. The modeled effects of many road characteristics were found to match intuitive expectations, suggesting that they were well-controlled in our assessment of the ISAs’ impacts. It was also confirmed that the incentive ISA had a large speed reduction effect on the behavior of drivers who tended to speed, while the informative ISA did not. In particular, although the impact of the incentive ISA on all drivers was only 2 km/h greater on average than that of the informative ISA, the impact of the incentive ISA on drivers who tended to speed was 7 km/h greater than that of the informative ISA when correlated random effects were considered.



中文翻译:

考虑个人惯常的超速倾向的分层贝叶斯建模评估智能速度适应的影响:一种相关的随机参数方法

尽管已经有大量的短期和长期现场试验以及基于驾驶模拟器的智能速度自适应(ISA)研究,但是评估ISA的影响的方法数量有限。特别是,通过现场试验适当评估ISA的影响需要考虑许多与详细道路特征相关的因素。此外,由于ISA本身具有“目标速度”功能,因此有必要考虑各个驾驶员通常的超速驾驶趋势,以免低估ISA的影响。在这项研究中,建立了具有相关随机效应的分层贝叶斯模型,以基于驾驶员的典型超速趋势和道路特征条件为混杂因素,研究了ISA对驾驶员速度的影响。该模型用于根据实地试验收集的数据,澄清和比较信息性和激励性ISA对速度限制为30 km / h(“ Zone 30”)的社区街道的影响。我们发现,许多道路特征的模拟效果都符合直觉的期望,这表明在我们对ISA的影响评估中,它们得到了很好的控制。还证实了,激励性ISA对趋于超速的驾驶员的行为具有很大的减速效果,而信息性ISA没有。特别是,尽管激励性ISA对所有驾驶员的影响平均仅比信息性ISA高2 km / h,但激励性ISA对倾向于超速驾驶的驾驶员的影响却比提示性ISA大7 km / h。当考虑相关随机效应时,信息性ISA。

更新日期:2020-02-25
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