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提出了一种基于线圈数据的瓶颈点自动识别算法.算法以临界流量作为算法的触发变量,根据道路条件、服务水平和大型车比例计算临界流量.算法的识别程序包括2部分:首先通过计算当前占有率与前时刻占有率的相对差值来判定瓶颈点上游位置;然后通过计算上游占有率与下游占有率的相对差值确定瓶颈点下游的位置.此外,提出了基于数据集计周期、瓶颈点识别率和误判率的算法性能评价方法.利用上海市内环高架大柏树-广中路段的线圈数据进行试验,结果表明,瓶颈点自动识别算法在准确率和效率上有显著提高.
An algorithm of bottleneck point automatic identification based on coil data is proposed.The algorithm uses critical traffic as the trigger variable of the algorithm and calculates the critical traffic according to the road conditions, service level and the proportion of large vehicles.The recognition program of the algorithm includes two parts: firstly, And the share of the former possession rate to determine the position of the bottleneck upstream; and then calculate the downstream position of the bottleneck point by calculating the relative difference between the upstream share and the downstream share.Furthermore, Point recognition rate and false positive rate.Using the coil data of the large cypress-green section of Inner Ring elevated in Shanghai, the experimental results show that the algorithm of bottleneck point automatic recognition has significantly improved in accuracy and efficiency.