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Using stop bar detector information to determine turning movement proportions in shared lanes

机译:使用停止杆检测器信息确定共享车道中的转弯运动比例

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摘要

Turning vehicle volumes at signalized intersections are critical inputs for various transportation studies such as level of service, signal timing, and traffic safety analysis. There are various types of detectors installed at signalized intersections for control and operation. These detectors have the potential of producing volume estimates. However, it is quite a challenge to use such detectors for conducting turning movement counts in shared lanes. The purpose of this paper was to provide three methods to estimate turning movement proportions in shared lanes. These methods are characterized as flow characteristics (FC), volume and queue (VQ) length, and network equilibrium (NE). FC and VQ methods are based on the geometry of an intersection and behavior of drivers. The NE method does not depend on these factors and is purely based on detector counts from the study intersection and the downstream intersection. These methods were tested using regression and genetic programming (GP). It was found that the hourly average error ranged between 4 and 27% using linear regression and 1 to 15% using GP. A general conclusion was that the proposed methods have the potential of being applied to locations where appropriate detectors are installed for obtaining the required data. Copyright (C) 2016 John Wiley & Sons, Ltd.
机译:在信号交叉路口转弯车辆的体积是各种交通运输研究(例如服务水平,信号定时和交通安全分析)的关键输入。在信号交叉口安装了各种类型的检测器,用于控制和操作。这些检测器具有产生体积估计的潜力。但是,使用这种检测器在共享车道上进行转弯运动计数是一个很大的挑战。本文的目的是提供三种方法来估计共享车道上的转弯运动比例。这些方法的特征是流量特征(FC),体积和队列(VQ)长度以及网络平衡(NE)。 FC和VQ方法基于交叉点的几何形状和驾驶员的行为。 NE方法不依赖于这些因素,而仅基于研究交叉口和下游交叉口的检测器计数。这些方法使用回归和遗传编程(GP)进行了测试。使用线性回归发现小时平均误差在4%到27%之间,而使用GP则为1%到15%。总体结论是,所提出的方法有可能被应用于安装了适当探测器以获取所需数据的位置。版权所有(C)2016 John Wiley&Sons,Ltd.

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