Research on Taxi Operation Characteristics by Improved DBSCAN Density Clustering Algorithm and K-means Clustering Algorithm

Abstract With the development of urbanization, the problem of urban traffic congestion is becoming more and more serious. An improved k-means clustering algorithm was proposed to solve the problem that the traditional k-means clustering center could easily be affected by the clustering center and fall into the local optimal solution. Based on the big data of New York City taxis, the operational characteristics are analyzed. The experimental results show that the improved K-means clustering algorithm has a better clustering analysis effect in terms of hot demand for taxis.