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Received:February 13, 2020
Received:February 13, 2020
中文摘要: 近年来城市的高速发展，使得城市的范围不断扩大，布局不断变化。搭乘出租车是市民出行的重要交通方式。不同的出租车司机对于城市的熟悉程度不同，选取载客点时会有不同倾向。选取正确的载客点，不仅可以缓解城市交通问题，而且对司机的收入影响较大。提出了基于时空数据挖掘的出租车司机载客点推荐算法，首先用HITS-K算法对城市内的不同时段进行热点地区聚类，再对出租车司机的日常活动模式进行分析，最后根据热点区域聚类与出租车司机的活动模式对载客点进行推荐。
Abstract:In recent years, with the rapid development of the city, the scope and layout of the city are constantly expanding.Taking a taxi is an important means of transportation in the city.Different taxi drivers have different familiarity with the city, so they have different tendency when choosing the passenger point.Choosing the right passenger point can not only alleviate the urban traffic problems, but also affect the income of drivers.Therefore, recommending pick-up points algorithm for taxi drivers is proposed.Firstly, HITS-K algorithm is used to cluster hot spots in different periods of the city.Then it analyzes the daily activity mode of taxi drivers.Finally, according to the hot spot region clustering and the taxi driver's activity mode, the passenger carrying points are recommended.
keywords: taxi trajectory hotspot area K-means algorithm
文章编号：20210616 中图分类号：TP311.13 文献标志码：
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