A GPS data based distributed K-means for cabstand location selection

摘要

Taxi has become an important component of public transportation system. A proper cabstand location can alleviate the traffic pressure. In this paper, a large set of global positioning system (GPS) data of taxi in Jinan City is employed to help locating the cabstand. By analyzing more than 300 million taxi driving data in Jinan, Shandong Province, the parallel K- means algorithm is applied on the cluster analysis based on Spark distributed computing framework. Based on the clustering results, the characteristics of taxi passengers are revealed. and the traffic hot spot map of taxi operation is generated according to visualized data results, which provides technical support for the selection of cabstand location. Although the results and conclusion are specific to Jinan City, the methods and models used in this paper can be employed on other cities as well.