Interval data‐based k ‐means clustering method for traffic state identification at urban intersections
Interval data‐based k ‐means clustering method for traffic state identification at urban intersections
复制标题
基于区间数据的k—means聚类方法进行城市路口交通状态识别
DOI：
10.1049/iet-its.2018.5379
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发表时间：
2019
影响因子：
2.7
通讯作者：
Zhenbo Lu
中科院分区：
文献类型：
--
作者：
Wenming Rao;Jingxin Xia;Weitao Lyu;Zhenbo Lu
识别城市十字路口的交通状态对于充分发挥智能交通系统在各种交通应用中的潜力（例如实时交通信号控制）起着重要作用。许多交通状态识别方法使用测量或估计的交通流变量的平均值来识别交通状态；然而，缺乏对交通流不确定性的考虑会导致识别不准确。为了研究交通流不确定性对交叉口交通状态的影响，本文提出了一种基于区间数据的k均值聚类方法来识别城市交叉口的交通状态。三个交通流变量（容量与容量比、队列长度和延迟）的不确定性以区间数据的形式表示并用作输入变量。所提出的方法已在中国昆山的真实交通网络上实施。测试结果表明，聚类结果具有可解释性，能够准确描述交通状态演化趋势。进一步的研究表明，所提出的方法优于基于均值的方法，并且在使用区间数据后队列长度对聚类结果有更显着的贡献。本研究的结果证明了所提出的方法在城市交叉口交通状态识别中的有效性。
Identifying traffic states at urban intersections plays a significant role in achieving the full potential of intelligent transportation systems for various traffic applications (e.g. real‐time traffic signal control). Many traffic state identification methods use mean values of measured or estimated traffic flow variables to identify traffic states; however, lacking a consideration of traffic flow uncertainty leads to inaccurate identifications. To investigate the impact of traffic flow uncertainty on intersection traffic states, this article proposes an interval data‐basedk‐means clustering method for traffic state identification at urban intersections. The uncertainty of three traffic flow variables (volume to capacity ratio, queue length, and delay) are represented in the form of interval data and employed as input variables. The proposed method was implemented on a real‐world traffic network in Kunshan, China. Test results show that the clustering results are explicable and can accurately describe the trend of traffic state evolution. Further investigation shows that the proposed method outperforms the mean‐value‐based method, and queue length has a more significant contribution to the clustering results after the use of interval data. The findings of this study demonstrate the effectiveness of the proposed method in traffic states identification at urban intersections.