Effects of road infrastructure and traffic complexity in speed adaptation behaviour of distracted drivers
Effects of road infrastructure and traffic complexity in speed adaptation behaviour of distracted drivers
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DOI：
10.1016/j.aap.2017.01.018
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发表时间：
2017-04-01
影响因子：
5.9
通讯作者：
Washington, Simon
中科院分区：
文献类型：
--
作者：
Oviedo-Trespalacios, Oscar;Hague, Md. Mazharul;Washington, Simon
驾驶时使用移动的电话仍然是交通系统中的主要人为因素问题。一个重要的安全问题是，驾驶时被移动的电话分心，可能会改变驾驶速度，导致与其他道路使用者发生冲突，从而增加撞车风险。然而，缺乏系统的知识，在速度适应分心的司机参与的机制限制了解释和建模的程度，这种现象。本研究的目的是调查分心驾驶员在不同的道路基础设施和交通复杂性条件下的速度适应。CARRS-Q高级驾驶模拟器用于在具有不同交通条件的模拟道路上对参与者进行测试，例如沿沿着直道的自由流动交通、在城市化地区驾驶以及沿沿着郊区道路在交通繁忙的情况下驾驶。32名有执照的年轻司机在三种电话条件下驾驶模拟器：基线（没有电话交谈），免提和手持电话交谈。为了了解分心，道路基础设施和交通复杂性之间的关系，速度适应计算为驾驶速度从张贴的速度限制的偏差使用决策树建模。从决策树中识别出的道路基础设施和交通特征组，然后用广义线性混合模型（GLMM）进行建模，重复测量，以推断分心驾驶员的速度适应行为。GLMM还包括驾驶员特征和次要任务需求作为速度适应的预测因子。结果表明，复杂的道路环境，如城市化，汽车跟随情况沿着郊区道路，和弯曲的道路线形显着影响速度适应行为。分心的司机选择一个较低的速度，而驾驶沿着弯曲的道路或在汽车以下的情况下，但速度适应是可以忽略不计的存在下，高视觉刀具，表示优先的驾驶任务的次要任务。此外，那些在自我报告的对移动的手机使用的安全态度上得分高的司机，以及那些报告先前参与过道路交通事故的司机，在分心的情况下选择了比基线更低的驾驶速度。研究结果有助于理解驾驶任务需求如何影响分心驾驶员在各种道路基础设施和交通复杂性条件下的速度适应。(C)2017爱思唯尔有限公司版权所有。
The use of mobile phones while driving remains a major human factors issue in the transport system. A significant safety concern is that driving while distracted by a mobile phone potentially modifies the driving speed leading to conflicts with other road users and consequently increases crash risk. However, the lack of systematic knowledge of the mechanisms involved in speed adaptation of distracted drivers constrains the explanation and modelling of the extent of this phenomenon. The objective of this study was to investigate speed adaptation of distracted drivers under varying road infrastructure and traffic complexity conditions. The CARRS-QAdvanced Driving Simulator was used to test participants on a simulated road With different traffic conditions, such as free flow traffic along straight roads, driving in urbanized areas, and driving in heavy traffic along suburban roads. Thirty-tWo licensed young drivers drove the simulator under three phone conditions: baseline (no phone conversation), hands -free and handheld phone conversations. To understand the relationships between distraction, road infrastructure and traffic complexity, speed adaptation calculated as the deviation of driving speed from the posted speed limit was modelled using a decision tree. The identified groups of road infrastructure and traffic characteristics from the decision tree were then modelled with a Generalized Linear Mixed Model (GLMM) with repeated measures to develop inferences about speed adaptation behaviour of distracted drivers. The GLMM also included driver characteristics and secondary task demands as predictors of speed adaptation. Results indicated that complex road environments like urbanization, car -following situations along suburban roads, and curved road alignment significantly influenced speed adaptation behaviour. Distracted drivers selected a lower speed while driving along a curved road or during car following situations, but speed adaptation was negligible in the presence of high visual cutter, indicating the prioritization of the driving task over the secondary task. Additionally, drivers who scored high on self -reported safe attitudes towards mobile phone usage, and who reported prior involvement in a road traffic crash, selected a lower driving speed in the distracted condition than in the baseline. The results aid in understanding how driving task demands influence speed adaptation of distracted drivers under various road infrastructure and traffic complexity conditions. (C) 2017 Elsevier Ltd. All rights reserved.