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Normal Deceleration Behavior of Passenger Vehicles at Stop Sign-Controlled Intersections Evaluated with In-Vehicle Global Positioning System Data

机译:车载全球定位系统数据评估的客车在停车标志控制的交叉口的正常减速行为

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摘要

Deceleration characteristics of passenger cars are often used in traffic simulation, vehicle fuel consumption and emissions models, and intersection and deceleration-lane design. Most previous studies collected spot speed data with detectors or radar guns. Because of the limitations of the data collection methods, these studies could not determine when and where drivers began to decelerate. Therefore, the studies may not provide an accurate estimation of deceleration time and distance. Furthermore, most previous studies are based on outdated and limited data, and their conclusions may not be applicable to the current vehicle fleet and drivers. The normal deceleration behavior of current passenger vehicles is evaluated at stop sign-controlled intersections on urban streets on the basis of in-vehicle Global Positioning System data. This study determined that drivers with higher approach speeds decelerated over a longer time and distance. Higher initial deceleration rates were also associated with higher approach speeds. However, the collected data in this study did not indicate a clear relationship between the average and maximum deceleration rates and approach speeds. With second-by-second deceleration profile data, the authors found that most drivers reached their maximum deceleration rates about 5 s or less than 5 s before stopping, and the maximum deceleration rate (3.4 m/s~2) recommended by AASHTO was applicable to most of the study drivers. This review verified several previous deceleration models with the current observations and found that the polynomial model developed by Akcelik and Biggs provides the best fit for the data set in this study. Finally, this study developed a new deceleration model based on the approach speeds and deceleration time.
机译:乘用车的减速特性通常用于交通仿真,车辆油耗和排放模型以及交叉口和减速车道设计中。以前的大多数研究都使用探测器或雷达枪收集了点速度数据。由于数据收集方法的局限性,这些研究无法确定驾驶员何时何地开始减速。因此,研究可能无法提供减速时间和距离的准确估计。此外,大多数先前的研究都是基于过时且有限的数据,其结论可能不适用于当前的车队和驾驶员。根据车载全球定位系统数据,在城市街道上停车牌控制的十字路口评估当前乘用车的正常减速行为。这项研究确定,具有较高进近速度的驾驶员会在更长的时间和距离上减速。较高的初始减速率也与较高的进近速度有关。但是,本研究中收集的数据并未表明平均和最大减速度与进近速度之间存在明确的关系。利用每秒的减速曲线数据,作者发现大多数驾驶员在停车前达到其最大减速率约5 s或少于5 s,并且AASHTO建议的最大减速率(3.4 m / s〜2)适用对于大多数学习驱动程序。这篇评论使用当前的观察结果验证了先前的几种减速模型,并发现由Akcelik和Biggs开发的多项式模型最适合本研究中的数据集。最后,本研究基于进近速度和减速时间开发了一种新的减速模型。

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