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首页> 外文期刊>Mathematical Problems in Engineering: Theory, Methods and Applications >A Road Network Traffic State Identification Method Based on Macroscopic Fundamental Diagram and Spectral Clustering and Support Vector Machine
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A Road Network Traffic State Identification Method Based on Macroscopic Fundamental Diagram and Spectral Clustering and Support Vector Machine

机译：一种基于宏观基础图和光谱聚类和支持向量机的道路网络流量状态识别方法

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摘要

Accurate identification of road network traffic status is the key to improve the efficiency of urban traffic control and management. Both data mining method and MFD-based methods can divide the traffic state of road network, but each has its own advantages and disadvantages. The data mining method is oriented to traffic data with high efficiency, but it can only discriminate traffic status from microlevel, while the MFD of road network can discriminate traffic status from macrolevel, but there are still some problems, such as the fact that the discriminant method of equivalence points based on MFD lacks theoretical support or that traffic status could not be subdivided. If data mining methods and road network’s MFD are combined, the accuracy of road network traffic state identification will be greatly improved. In addition, the research shows that the combination of unsupervised learning clustering analysis method (such as spectral clustering algorithm) and supervised learning machine algorithm (such as support vector machine algorithm (SVM)) is more accurate in traffic state identification. Therefore, a traffic state identification method based on MFD and spectral clustering and SVM is proposed, combining the advantages of spectral clustering algorithm and SVM algorithm. Firstly, spectral clustering algorithm is used to classify the traffic state of road network’s MFD. Secondly, SVM multiclassifier is trained with the partitioned road network’s MFD parameters, and the accuracy evaluation method of classification results based on obfuscation matrix is given. Finally, the connected-vehicle network simulation platform is built for empirical analysis. The results show that the classification results of spectral clustering algorithm are closer to the theoretical values, compared with K-means algorithm, and the accuracy of SVM multiclassifier is 96.3%. It can be seen that our algorithm can identify the road network traffic state more effectively from the macrolevel.
机译：准确识别道路网络交通状况是提高城市交通管制和管理效率的关键。数据挖掘方法和基于MFD的方法都可以划分道路网络的交通状态，但每个都有自己的优缺点。数据挖掘方法以高效率为导向到交通数据，但它只能区分从MicroLevel的流量状态，而道路网络的MFD可以区分来自MacRolevel的交通状态，但仍存在一些问题，例如判别的事实基于MFD的等效点的方法缺乏理论支持或无法细分的交通状态。如果组合数据挖掘方法和道路网络的MFD，则会大大提高道路网络流量状态识别的准确性。此外，该研究表明，无监督的学习聚类分析方法（如光谱聚类算法）和监督学习机算法（例如支持向量机算法（SVM））的组合在交通状态识别中更准确。因此，提出了一种基于MFD和光谱聚类和SVM的交通状态识别方法，组合了谱聚类算法和SVM算法的优点。首先，使用光谱聚类算法来分类道路网络的MFD的交通状态。其次，通过分区道路网络的MFD参数培训SVM多批读，给出了基于混淆矩阵的分类结果的精度评估方法。最后，建立了连接车辆网络仿真平台以进行实证分析。结果表明，与K均值算法相比，光谱聚类算法的分类结果更接近理论值，SVM多批变器的精度为96.3％。可以看出，我们的算法可以从MacRoLevel更有效地识别道路网络流量状态。

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