K-means station region division method based on space-time influence distance

The invention relates to a K-means station region division method based on a space-time influence distance, and the method comprises the steps: S100, obtaining the travel data of a certain urban railtransit, and preprocessing the data; S200, taking all stations xi as a clustering data set omega, randomly selecting one station as an initial clustering center, and then sequentially selecting K clustering centers; S300, calculating the space-time influence distance from each station to each clustering center point and if the space-time influence distance from the station to which clustering center point is minimum, classifying the stations into the cluster of the clustering center; S400, for each re-divided category, newly calculating a clustering center of the category; and S500, repeatingthe steps S300 and S400 until the position of the clustering center of each category is not changed any more, and outputting the clustering center and all stations in each category. The method provided by the invention not onl