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Modeling and empirical analysis of tailgating behavior of drivers.

机译：驾驶员尾随行为的建模和实证分析。

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摘要

This dissertation presents a microscopic study of tailgating behavior of drivers. There are very few studies focused on tailgating, although it is a serious issue for traffic safety. The reason for very few studies might be the fact that tailgating is a complex problem involving human behavior and kinematics of the vehicle and it is also equally challenging to collect naturalistic driving data relevant to tailgating.;Because this approach is empirical, we developed a sophisticated data acquisition system using an instrumented vehicle to collect naturalistic driving data. Data were collected on freeways in Maryland during times of moderate traffic flow. The instrumented vehicle was driven in a naturalistic way that was benign to the surrounding traffic. Tailgating events were detected using the empirical data and a model of safe following distance.;We tested and affirmed the hypothesis that tailgaters of short tailgating duration are more willing to follow at close following distances than those who tailgated for longer durations. We also tested and affirmed the hypothesis that following vehicle speeds are strongly influenced by lead vehicle speeds. We studied the causal relations between certain observable data from the lead vehicle and possible reactions in the following vehicle.;We contributed new estimates of driver reaction times, focusing on a subset of the population deemed to be tailgating at the time. We also conducted a new calibration of the well-known GHR car-following model that is specific to tailgating situations.;The data and method for collecting the data are contemporary and relevant to current modes of thinking in traffic flow theory. The results can contribute directly to models and parameter estimates in microscopic simulators. Many of the results would also be of use in the automotive industry, for the development of driver safety assistance systems and countermeasures. Finally, we think the results could be useful for driving instructors, to help students understand better this dangerous driving behavior. In the end, we hope that this study could help to improve traffic safety by reducing the number of crashes resulting from this behavior.
机译：本文对驾驶员的尾随行为进行了微观研究。尽管这对交通安全是一个严重的问题，但很少有研究关注补时。很少进行研究的原因可能是，尾随是涉及人类行为和车辆运动学的复杂问题，并且收集与尾随相关的自然驾驶数据也同样具有挑战性。由于这种方法是经验性的，因此我们开发了一种复杂的方法。数据采集​​系统，使用仪表车辆收集自然驾驶数据。在交通流量适中的时候，在马里兰州的高速公路上收集了数据。仪表车以对周围交通无害的自然方式驾驶。使用经验数据和安全跟随距离模型检测尾部事件。；我们测试并确认了以下假设：与持续时间较长的那些尾门相比，较短尾翼持续时间的尾门更愿意跟随近距离。我们还测试并确认了以下假设：跟随车速受领先车速的强烈影响。我们研究了来自领先车辆的某些可观察数据与随后车辆中可能发生的反应之间的因果关系。;我们对驾驶员反应时间进行了新的估算，着眼于当时被视为拖尾的总体子集。我们还对尾随情况特有的著名GHR汽车跟随模型进行了新的校准。数据和收集数据的方法是现代的，并且与交通流理论中的当前思维方式有关。结果可以直接有助于微观模拟器中的模型和参数估计。许多结果也将在汽车工业中用于驾驶员安全辅助系统和对策的开发。最后，我们认为结果可能对驾驶教练有用，可以帮助学生更好地了解这种危险的驾驶行为。最后，我们希望这项研究可以通过减少这种行为导致的撞车次数来帮助改善交通安全。

著录项

  • 作者

    Shrestha, Deepak Kumar.;

  • 作者单位

    University of Maryland, College Park.;

  • 授予单位 University of Maryland, College Park.;
  • 学科 Engineering Civil.;Transportation.
  • 学位 Ph.D.
  • 年度 2009
  • 页码 169 p.
  • 总页数 169
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 建筑科学;综合运输;
  • 关键词

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