The interrelationships between speed limits, geometry, and driver behavior: a proof-of-concept study utilizing naturalistic driving data
The interrelationships between speed limits, geometry, and driver behavior: a proof-of-concept study utilizing naturalistic driving data
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速度限制、几何形状和驾驶员行为之间的相互关系：利用自然驾驶数据的概念验证研究
DOI：
10.31274/etd-180810-4594
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发表时间：
2016
期刊：
影响因子：
--
通讯作者：
Raha Hamzeie
中科院分区：
文献类型：
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作者：
Raha Hamzeie
自20世纪60年代的S以来，速度管理一直是交通安全研究的广泛焦点。研究普遍表明，随着交通平均速度的增加，以及车流中行驶速度的标准差增加，碰撞风险会增加。然而，关于限速的影响的研究在某种程度上是不确定的。这项研究调查了速度限制如何影响司机的速度选择，以及由此产生的撞车风险，同时控制各种混杂因素，如交通量和道路几何形状。数据以非常高的分辨率从作为第二战略公路研究计划(SHRP 2)的一部分进行的自然主义驾驶研究(NDS)中获得。这些数据与道路信息数据库(RID)集成在一起，该数据库提供有关六个州研究区域(佛罗里达州、印第安纳州、纽约州、北卡罗来纳州、宾夕法尼亚州和华盛顿州)道路特征的广泛详细信息。这些来源被用来检查司机的速度选择如何在具有不同张贴速度限制的高速公路之间变化，以及撞车/险些撞车事件的可能性如何相对于各种速度指标发生变化。回归模型估计用于评估三个重要指标：碰撞前、接近碰撞和基准(即正常)驾驶事件期间车辆的平均速度；通过这段时间内的速度标准偏差(即平均加速/减速率)量化的导致每个事件的行驶速度的变化；以及基于速度选择和其他显著因素的特定事件导致碰撞或险些碰撞的可能性。
Speed management has been an extensive focus of traffic safety research dating back to the 1960’s. Research has generally shown crash risk to increase as the average speed of traffic increases and as the standard deviation of travel speeds increases within a traffic stream. However, research as to the effects of speed limits has been somewhat inconclusive. This study investigates how speed limits affect driver speed selection, as well as the resultant crash risk, while controlling for various confounding factors such as traffic volumes and roadway geometry. Data are obtained at very high resolution from a Naturalistic Driving Study (NDS) conducted as a part of the second Strategic Highway Research Program (SHRP 2). These data are integrated with a Roadway Information Database (RID), which provides extensive details as to roadway characteristics in the six-state study area (Florida, Indiana, New York, North Carolina, Pennsylvania, and Washington.) These sources are used to examine how driver speed selection varies among freeways with different posted speed limits, and how the likelihood of crash/near-crash events change with respect to various speed metrics. Regression models are estimated to assess three measures of interest: the average speed of vehicles during the time preceding crash, near-crash, and baseline (i.e., normal) driving events; the variation in travel speeds leading up to each event as quantified by the standard deviation in speeds over this period (i.e. the average acceleration/deceleration rate); and the probability of a specific event resulting in a crash or near-crash based on speed selection and other salient factors.