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Do High Visibility Enforcement programs affect aggressive driving behavior? An empirical analysis using Naturalistic Driving Study data

机译:高可见性执法计划是否会影响积极的驾驶行为?使用自然驾驶研究数据的实证分析

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摘要

This paper investigates the effect of High Visibility Enforcement (HVE) programs on different types of aggressive driving behavior, namely, speeding, tailgating, unsafe lane changes and 'other' aggressive driving behavior types (occurrence of not-yielding right-of-way and red light or stop signs violations). For this purpose, the Second Strategic Highway Research Program (SHRP2) Naturalistic Driving Study (NDS) data are used, which include forward-facing videos and time series information with regard to trips conducted at or near the locations of HVE implementation. To capture the intensity and duration of speeding and tailgating, scaled metrics are developed. These metrics can capture varying levels of aggressive driving behavior enabling, thus, a direct comparison of the various behavioral aspects over time and among different drivers. To identify the effect of HVE and other trip, driver, vehicle or environmental factors on speeding and tailgating, while accounting for possible interrelationship among the behavior-specific scaled metrics, Seeming Unrelated Regression Equation (SURE) models were developed. To analyze the likelihood of occurrence of unsafe lane changes and 'other' aggressive driving behavior types, a grouped random parameters ordered probit model with heterogeneity in means and a correlated grouped random parameters binary logit model were estimated, respectively. The results showed that drivers' awareness of HVE implementation has the potential to decrease aggressive driving behavior patterns, especially unsafe lane changes and 'other' aggressive driving behaviors.
机译:本文调查了高可见性执行(HVE)方案对不同类型的侵略性驾驶行为的影响,即超速,尾随,不安全的车道变化和“其他”侵略性驾驶行为类型(不屈服的出现红灯或停止迹象违规)。为此目的,使用第二战略公路研究计划(SHRP2)自然驾驶研究(NDS)数据,其中包括前瞻性视频和时间序列信息,关于在HVE实现的位置进行或附近进行的旅行。为了捕获超速和尾随的强度和持续时间,开发了缩放度量。这些度量可以捕获不同级别的激进驾驶行为,从而实现了各个行为方面随时间和不同驱动器的各种行为方面的直接比较。为了识别HVE和其他跳闸,驾驶员,车辆或环境因素对超速和尾随的影响,同时占特定行为缩放度量的可能性相互关系,似乎开发了不相关的回归方程(SEQUE)模型。为了分析不安全车道变化和“其他”侵略性驾驶行为类型的发生的可能性,分别估计了具有异质性的分组随机参数有序概率模型和相关的分组随机参数二进制记录模型。结果表明,司机对HVE实施的认识有可能降低积极的驾驶行为模式,特别是不安全的车道变化和“其他”侵略性的驾驶行为。

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