Rank of Hangzhou Public Free-Bicycle System rent stations by improved k-means clustering

Abstract

In China, Hangzhou is the first city to set up the Public Free-Bicycle System. There are many and many technology problems in the decision of intelligent dispatch. In this paper, we investigate the rank of Hangzhou Public Free-Bicycle System rent station with improved k-means clustering. Actually, ranking rent station is a very challenge work. In this paper, an improved k-means clustering algorithm is proposed for efficient getting the rank of Hangzhou Public Free-Bicycle System rent s-tations. At first, by passing over the cruel one week's database, a rent-return database is initialed. Then, the rank is determined from the borrow-return database.