School of Transportation Science and Engineering, Harbin Institute of Technology, 73 Huanghe Street, Nangang District, Harbin, China.
USDOT Center for Advanced Multimodal Mobility Solutions and Education (CAMMSE), Department of Civil and Environmental Engineering, University of North Carolina at Charlotte, EPIC Building, Room 3366, 9201 University City Boulevard, Charlotte, NC 28223-0001.
J Safety Res. 2021 Dec;79:199-210. doi: 10.1016/j.jsr.2021.09.001. Epub 2021 Sep 17.
With prevalent and increased attention to driver inattention (DI) behavior, this research provides a comprehensive investigation of the influence of built environment and roadway characteristics on the DI-related vehicle crash frequency per year. Specifically, a comparative analysis between DI-related crash frequency in rural road segments and urban road segments is conducted.
Utilizing DI-related crash data collected from North Carolina for the period 2013-2017, three types of models: (1) Poisson/negative binomial (NB) model, (2) Poisson hurdle (HP) model/negative binomial hurdle (HNB) model, and (3) random intercepts Poisson hurdle (RIHP) model/random intercepts negative binomial hurdle (RIHNB) model, are applied to handle excessive zeros and unobserved heterogeneity in the dataset.
The results show that RIHP and RIHNB models distinctly outperform other models in terms of goodness-of-fit. The presence of commercial areas is found to increase the probability and frequency of DI-related crashes in both rural and urban regions. Roadway characteristics (such as non-freeways, segments with multiple lanes, and traffic signals) are positively associated with increased DI-related crash counts, whereas state-secondary routes and speed limits (higher than 35 mph) are associated with decreased DI-related crash counts in rural and urban regions. Besides, horizontal curved and longitudinal bottomed segments and segments with double yellow lines/no passing zones are likely to have fewer DI-related crashes in urban areas. Medians in rural road segments are found to be effective to reduce DI-related crashes. Practical Applications: These findings provide a valuable understanding of the DI-related crash frequency for transportation agencies to propose effective countermeasures and safety treatments (e.g., dispatching more police enforcement or surveillance cameras in commercial areas, and setting more medians in rural roads) to mitigate the negative consequences of DI behavior.
随着人们对驾驶注意力不集中（DI）行为的普遍关注和日益增加，本研究全面调查了建成环境和道路特征对每年与 DI 相关的车辆碰撞频率的影响。具体来说，对农村道路段和城市道路段与 DI 相关的碰撞频率进行了比较分析。
利用北卡罗来纳州 2013-2017 年收集的与 DI 相关的碰撞数据，应用了三种模型：（1）泊松/负二项式（NB）模型，（2）泊松障碍（HP）模型/负二项式障碍（HNB）模型，以及（3）随机截距泊松障碍（RIHP）模型/随机截距负二项式障碍（RIHNB）模型，以处理数据集中过多的零值和未观察到的异质性。
结果表明，RIHP 和 RIHNB 模型在拟合优度方面明显优于其他模型。研究发现，商业区域的存在增加了农村和城市地区与 DI 相关的碰撞的概率和频率。道路特征（如非高速公路、多车道路段和交通信号）与增加的与 DI 相关的碰撞计数呈正相关，而州际次要道路和限速（高于 35 英里/小时）与农村和城市地区与 DI 相关的碰撞计数减少呈负相关。此外，水平弯曲和纵向底部路段以及有双黄线/无超车区的路段在城市地区与 DI 相关的碰撞较少。在农村道路段中发现中央分隔带可有效减少与 DI 相关的碰撞。
这些发现为交通管理机构提供了对与 DI 相关的碰撞频率的深入了解，以便提出有效的对策和安全措施（例如，在商业区域增加更多的警察执法或监控摄像头，以及在农村道路设置更多的中央分隔带），以减轻 DI 行为的负面影响。
