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首页> 外文期刊>Accident Analysis & Prevention >Assessment of freeway traffic parameters leading to lane-change related collisions.
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Assessment of freeway traffic parameters leading to lane-change related collisions.

机译：评估导致车道变更相关碰撞的高速公路交通参数。

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This study aims at 'predicting' the occurrence of lane-change related freeway crashes using the traffic surveillance data collected from a pair of dual loop detectors. The approach adopted here involves developing classification models using the historical crash data and corresponding information on real-time traffic parameters obtained from loop detectors. The historical crash and loop detector data to calibrate the neural network models (corresponding to crash and non-crash cases to set up a binary classification problem) were collected from the Interstate-4 corridor in Orlando (FL) metropolitan area. Through a careful examination of crash data, it was concluded that all sideswipe collisions and the angle crashes that occur on the inner lanes (left most and center lanes) of the freeway may be attributed to lane-changing maneuvers. These crashes are referred to as lane-change related crashes in this study. The factors explored as independent variables include the parameters formulated to capture the overall measure of lane-changing and between-lane variations of speed, volume and occupancy at the station located upstream of crash locations. Classification tree based variable selection procedure showed that average speeds upstream and downstream of crash location, difference in occupancy on adjacent lanes and standard deviation of volume and speed downstream of the crash location were found to be significantly associated with the binary variable (crash versus non-crash). The classification models based on data mining approach achieved satisfactory classification accuracy over the validation dataset. The results indicate that these models may be applied for identifying real-time traffic conditions prone to lane-change related crashes.
机译：这项研究旨在使用从一对双回路检测器收集的交通监控数据来“预测”与车道变更相关的高速公路碰撞的发生。这里采用的方法涉及使用历史碰撞数据和从环路检测器获得的实时交通参数的相应信息来开发分类模型。用来校准神经网络模型的历史碰撞和环路检测器数据（对应于碰撞和非碰撞情况以建立二进制分类问题）是从奥兰多（FL）大都市区的州际4走廊收集的。通过仔细检查碰撞数据，可以得出结论，在高速公路的内部车道（最左侧和中部车道）上发生的所有侧滑碰撞和角度碰撞都可归因于变道演习。这些碰撞在本研究中被称为与车道变更相关的碰撞。作为独立变量探索的因素包括制定的参数，这些参数用于捕获在碰撞位置上游的车站的车道转换和车道之间速度，体积和占用率变化的整体度量。基于分类树的变量选择程序表明，碰撞位置上游和下游的平均速度，相邻车道上的占用率差异以及碰撞位置下游的速度和体积的标准偏差与二元变量（碰撞与非碰撞）显着相关。崩溃）。基于数据挖掘方法的分类模型在验证数据集上获得了令人满意的分类精度。结果表明，这些模型可用于识别易于发生与车道变更相关的碰撞的实时交通状况。

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