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A fuzzy weighted c-means classification method for traffic flow state division
Modern Physics Letters B ( IF 1.9 ) Pub Date : 2021-06-22 , DOI: 10.1142/s0217984921503413
Liangliang Zhang 1 , Yuanhua Jia 2 , Dongye Sun 3 , Yang Yang 4
Affiliation  

Traffic status recognition and classification is an important prerequisite for traffic management and control. Based on the idea of weight optimal, a weighted fuzzy c-means clustering method for improving the accuracy of traffic classification is proposed in this study to ease traffic congestion. First, since there are many indexes that affect the traffic flow state classification, three commonly used indexes namely, volume, speed and occupancy are chosen as the main parameters for the traffic flow state classification in this paper. Second, in order to quantitatively analyze the influence degree of different traffic flow parameters on traffic flow state division, based on the principle of weight optimization, the objective function of weight optimization is established. Then the weight of each attribute index is obtained by using the branch and bound algorithm. Finally, since the traditional fuzzy c-means clustering method will not consider the influence of different traffic flow parameter weights on the traffic flow state classification results, the classification effect needs to be further improved. A fuzzy weighted c-means classification method which uses weighted Euclidean distance instead of Euclidean distance is proposed to classify the traffic flow states. Based on the same traffic flow data sample on the same road section, the traffic state classification results with different methods show that it is helpful to improve the traffic flow state classification accuracy by weighting the clustering index. Because the influence of different parameters on the traffic flow state classification is considered in the process of clustering, it is more conducive to improve the classification accuracy. Moreover, it can provide more accurate classification information for traffic control and decision making.

中文翻译:

一种交通流状态划分的模糊加权c-means分类方法

交通状态识别和分类是交通管理和控制的重要前提。基于权重优化的思想,本文提出了一种加权模糊c-means聚类方法来提高交通分类的准确性,以缓解交通拥堵。首先,由于影响交通流状态分类的指标较多,本文选取交通量、速度和占用率三个常用指标作为交通流状态分类的主要参数。其次,为了定量分析不同交通流参数对交通流状态划分的影响程度,基于权重优化原理,建立了权重优化的目标函数。然后通过分支定界算法得到每个属性指标的权重。最后,由于传统的模糊c-means聚类方法不会考虑不同交通流参数权重对交通流状态分类结果的影响,分类效果有待进一步提高。提出了一种使用加权欧式距离代替欧式距离的模糊加权c-means分类方法对交通流状态进行分类。基于同一路段的相同交通流数据样本,不同方法的交通状态分类结果表明,通过加权聚类指标有助于提高交通流状态分类的准确性。由于在聚类过程中考虑了不同参数对交通流状态分类的影响,更有利于提高分类精度。此外,它可以为交通控制和决策提供更准确的分类信息。
更新日期:2021-06-22
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