This paper seeks to assess the effectiveness of high-visibility enforcement (HVE) programs in terms of reducing aggressive driving behavior. Using Strategic Highway Research Program 2 (SHRP2) Naturalistic driving study (NDS) data, behavioral reactions of drivers before, during, and after the conduct of high-visibility enforcement programs are analyzed, in order to identify the potential effect of high-visibility enforcement in driving behavior. In this context, two fundamental aspects of aggressive driving behavior (speeding and tailgating) are employed and analyzed. To simultaneously explore the intensity and the duration of these behavioral patterns, novel metrics are defined and used in the analysis. To investigate the effect of high-visibility enforcement programs, and at the same time, to control for the effect of driver-, trip-, vehicle-, and weather-specific characteristics on the extent of speeding and tailgating, univariate grouped random parameters linear regression models are estimated. In addition, likelihoods of speeding and tailgating occurrences are analyzed simultaneously, within a grouped random parameters bivariate probit modeling framework. The results of this preliminary analysis show that even though the implementation of the high-visibility enforcement has mixed effects on the extent and the likelihood of the driving behavior metrics, it demonstrates a promising potential in modifying driving behavior.

本文旨在评估高可见度执法（HVE）计划在减少攻击性驾驶行为方面的有效性。使用战略公路研究计划2（SHRP2）自然驾驶研究（NDS）数据，分析驾驶员在实施高可见性执法计划之前，之中和之后的行为反应，以便确定高可见性执法的潜在影响在驾驶行为上。在这种情况下，采用和分析了侵略性驾驶行为的两个基本方面（超速驾驶和尾随驾驶）。为了同时探索这些行为模式的强度和持续时间，定义了新的指标并将其用于分析中。调查高可见度执法计划的效果，同时控制驾驶员，旅途，车辆，并根据超速和追尾程度的特定天气特征，估计单变量分组随机参数线性回归模型。另外，在分组的随机参数双变量概率模型框架内，同时分析了超速和尾随发生的可能性。初步分析的结果表明，即使实施高可见度执法对驾驶行为量度的程度和可能性产生不同的影响，也显示出在改变驾驶行为方面的潜力。在分组的随机参数双变量概率模型框架内。初步分析的结果表明，即使实施高可见度执法对驾驶行为量度的程度和可能性产生不同的影响，也显示出在改变驾驶行为方面的潜力。在分组的随机参数双变量概率模型框架内。初步分析的结果表明，即使实施高可见度执法对驾驶行为量度的程度和可能性产生不同的影响，也显示出在改变驾驶行为方面的潜力。