计算机与现代化

• 算法设计与分析 • 上一篇    下一篇

基于出租车GPS轨迹和POI数据的商业选址推荐

  

  1. (西北师范大学数学与统计学院,甘肃兰州730070)
  • 收稿日期:2019-06-19 出版日期:2020-03-03 发布日期:2020-03-03
  • 作者简介:贾冲(1994-),男,甘肃武山人,硕士研究生,研究方向:统计学习及大数据分析,E-mail: 1792869340 @qq.com; 冯慧芳(1971-),女,教授,博士,CCF会员,研究方向:车载自组织网络,统计学习及大数据分析,E-mail: hffeng@nwnu.edu.cn; 杨振娟(1994-),女,硕士研究生,研究方向:统计学习及大数据分析,E-mail: 1297820384@qq.com。
  • 基金资助:
    国家自然科学基金资助项目(71761031,71561024) 

A Commercial Site Recommendation Algorithm Based on Taxi GPS Trajectory and POI Data 

  1. (College of Mathematics & Statistics, Northwest Normal University, Lanzhou 730070, China)
  • Received:2019-06-19 Online:2020-03-03 Published:2020-03-03

摘要: 针对商业选址问题,提出一种基于城市出租车GPS轨迹和POI数据的商业选址推荐算法。首先,对城市出租车GPS轨迹和POI数据进行预处理及地图匹配,然后将城区进行交通小区划分,用OD矩阵分析交通小区之间的交通流量特征,并结合交通小区内POI的分布特征和语义属性,构建基于OD矩阵和对应小区POI数据相结合的商业地址推荐模型。最后,应用兰州市出租车GPS轨迹与POI数据验证了推荐算法的有效性和实用性,并将推荐结果在交通小区尺度上进行可视化呈现。实验结果表明,该推荐算法不仅能够推荐合理的商业选址,为商业选址决策提供快速有效的可视化定量分析方法,同时能够为城市公共服务设施空间布局规划提供决策依据。

关键词: 商业选址, 推荐算法, 出租车轨迹, 兴趣点数据, 城市公共服务设施

Abstract: Focused on the issue of commercial site selection, a commercial site recommendation algorithm based on taxi GPS trajectory and POI of urban is proposed. Firstly, the taxi GPS trajectory and POI data of the city are preprocessed and map matching is performed. Secondly, we split the city into traffic zones and analyze the traffic flow characteristics between traffic zones by using OD matrix. Combining the POI distribution characteristics and semantic attributes in the traffic zones, a commercial site recommendation model based on the OD matrix and the POI data is constructed. Finally, the effectiveness and practicability of the proposed algorithm are verified by taxi GPS trajectory and POI data of Lanzhou. The recommendation results are visualized at the traffic zones level. The experimental results show that this recommendation algorithm can not only recommend reasonable commercial site selection, but also provide an immediately visual quantitative analysis in decision making for commercial site selection. At the same time, it can provide decision-making basis for urban public service facilities spatial layout planning.

Key words: commercial site selection, recommendation algorithm, taxi GPS trajectory, POI data, urban public service facilities

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