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首页> 外文期刊>Promet-traffic & transportation >NEW APPROACH TO ESTIMATING THE SATURATION FLOW RATE OF A SHARED LANE WITH PERMITTED LEFT TURNS
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NEW APPROACH TO ESTIMATING THE SATURATION FLOW RATE OF A SHARED LANE WITH PERMITTED LEFT TURNS

机译：估计共享通道的饱和流速的新方法，允许左转

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摘要

The estimation of the saturation flow rate is of utmost importance when defining the signal plan at intersections. Because of the numerous influential factors, the values of which are hard to be determined, the subject problem is to be regarded as an extremely complex one. This research deals with the estimation of a saturation flow rate of a shared lane with permitted left turns. The suggested algorithm is based on the application of the artificial neural networks where the data for training are received by simulation. The results obtained by the neural networks are compared with multiple linear regression and the known HCM 2010 approach for determining the saturated flow of a shared lane. The testing data have shown that the approach based on the artificial neural networks foresaw statistically significantly better values than the ones obtained by multiple linear regression, with an error of 27 veh/h against 49 veh/h. The HCM 2010 approach is significantly worse than the two others included in this research. The ways of the future development of the suggested method could include additional factors, such as the grade of the traffic lane, the proximity of the bus stops, and others.
机译：当在交叉点定义信号计划时，饱和流量的估计至关重要。由于许多有影响力的因素，因此难以确定的值，主题问题被认为是极其复杂的问题。该研究讨论了允许左转的共享通道的饱和流速的估计。建议的算法基于人工神经网络的应用，其中通过模拟接收训练数据。通过神经网络获得的结果与多元线性回归和已知的HCM 2010方法进行比较，用于确定共享通道的饱和流。测试数据表明，基于人工神经网络的方法统计上显着更好的值比多个线性回归所获得的值，具有27 VeL / H的误差。 HCM 2010方法比本研究中包含的两种方法差异很大。建议方法未来发展的方式可以包括其他因素，例如交通车道的等级，公交车站的附近等。

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