关键词: 数据挖掘, 聚类, 接力拼车, 豪斯多夫距离, 匹配度

Abstract: Carpooling is an environmentally friendly and energy saving way to travel. Excellent carpooling strategy can not only relieve the traffic pressure, optimize passengers’ experience, but also reduce carbon emissions and so on. To solve carpooling problem, this paper employs a two-stage carpool matching strategy. In the first stage, this paper employs the cluster method based on Hausdorff distance to assign travel demands to specific vehicle. And in the second stage, the cluster method based on matching degree is proposed to choose the most appropriate travel demands for every car. The experimental result shows that this method has achieved the goal to make recommendations for both single-vehicle carpooling and multi-vehicle carpooling.

Key words: data mining, clustering, multi-carpooling, Hausdorff distance, matching degree