The invention discloses an urban expressway traffic state recognition method based on machine learning. The method comprises the following steps of S1, obtaining flow, speed and occupancy parameter data influencing an expressway traffic state; S2, expressing a group of traffic flow parameters including speed, flow and occupancy on a space coordinate axis, randomly selecting a plurality of points as initial clustering centers, dividing a data set into four types, and randomly selecting four points as four types of initial clustering centers; S4, generating a plurality of chromosomes according to a set population size, performing fitness evaluation on each chromosome, constructing a fuzzy clustering traffic state division model combined with a genetic algorithm by adopting a strategy of clustering first and then classifying, clustering a large amount of data, and performing traffic state classification by using an SVM (Support Vector Machine) to obtain a classification result; therefore,classification boundaries can be found more easily, and data processing efficiency and classification accuracy are improved.
本发明公开了一种基于机器学习的城市快速路交通状态识别方法,包括以下步骤:S1:获取影响高速公路交通状态的流量、速度、占有率参数数据;S2:将一组含有速度、流量、占有率的交通流参数在空间坐标轴上表示出来,并随机选取若干个点作为初始聚类中心,并将数据集划分成4类,随机选取4个点为四类的初始聚类中心:S3:对选取的聚类中心进行实数值编码;S4:根据设定的种群规模大小生成若干条上述染色体,对每一条染色体进行适应度评价,采用先聚类后分类的策略,构建了结合遗传算法的模糊聚类交通状态划分模型,先对大量数据进行聚类预处理后,再使用SVM进行交通状态分类,使之更容易找到分类边界,提高数据处理效率和分类准确度。
Urban expressway traffic state recognition method based on machine learning
一种基于机器学习的城市快速路交通状态识别方法
2021-03-26
Patent
Electronic Resource
Chinese
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