Traffic Clustering Algorithm Based on Markov Distance and Canopy Improved K-means
Clustering algorithms are often used in the research of traffic data,and different clustering algorithms have differ-ent characteristics.As one of the clustering algorithms,K-means has high accuracy and practicability,but its accuracy is easily af-fected by subjective selection of K value and determination of initial clustering center.In order to optimize the selection of clustering center and K value,MC-Kmeans algorithm is proposed In the proposed method,firstly,the K value is selected by canopy algo-rithm,and then the initial cluster center is determined according to the calculation criterion of Mahalanobis distance.Finally,the K value and the value of cluster center are clustered as the parameters of K-means MC-Kmeans algorithm is applied to New York taxi traffic data in a certain period of time for practical verification.The results show that compared with K-means algorithm,the pro-posed method has higher accuracy,better matches the actual traffic situation,and can better reflect the traffic hot spots in the re-gion.
国家科技期刊平台
NSTL
万方数据
