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首页> 外文会议>The 2nd International Workshop on Artificial Intelligence and Mathematical Methods in Pavement and Geomechanical Systems, Aug 11-12, 2000, Newark, Delaware, USA >Modeling the 85th percentile speed on Oklahoma two-lane rural highways via neural network approach
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Modeling the 85th percentile speed on Oklahoma two-lane rural highways via neural network approach

机译：通过神经网络方法对俄克拉荷马州两车道农村公路的第85个百分位速度进行建模

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摘要

Traffic operations on two-lane rural highways and setting of realistic speed limits are some of the difficult tasks faced by state transportation agencies including the Oklahoma Department of Transportation (ODOT). Most traffic engineers believe that speed limits should be posted to reflect the maximum speed considered to be safe and reasonable by the majority of drivers using the roadway. Modeling traffic speeds using the classical mathematical approach does not frequently yield reliable results because of the human factors involved in driving. In recent years neural network (NN) approach has emerged as a powerful tool in solving many engineering problems where the physics of the problem are poorly understood or are not known because of the complexities involved. A neural network (backpropagation architecture) model is presented in this paper for the prediction of 85th percentile speed on Oklahoma two-lane rural highways. The model predicted the 85th percentile speed with an average degree of accuracy of about 96%.
机译：俄克拉荷马州交通运输部（ODOT）等州交通部门面临的一些艰巨任务是，在两条车道的农村公路上进行交通运营以及设定现实的限速。大多数交通工程师认为，应该张贴速度限制，以反映大多数使用道路的驾驶员认为安全合理的最大速度。由于涉及驾驶的人为因素，使用经典数学方法对交通速度进行建模通常不会产生可靠的结果。近年来，神经网络（NN）方法已成为解决许多工程问题的有力工具，在这些工程问题中，由于涉及的复杂性，对问题的物理知识了解甚少或未知。本文提出了一种神经网络（反向传播架构）模型，用于预测俄克拉荷马州两车道农村公路上第85个百分点的速度。该模型预测第85个百分位速度，平均准确度约为96％。

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