地球信息科学学报 ›› 2014, Vol. 16 ›› Issue (5): 720-726.doi: 10.3724/SP.J.1047.2014.00720

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基于语义轨迹停留点的位置服务匹配与应用研究

齐凌艳1,2(), 陈荣国1,,A;*(), 温馨1,2   

  1. 1. 中国科学院地理科学与资源研究所 资源与环境信息系统国家重点实验室,北京 100101
    2. 中国科学院大学,北京 100049
  • 收稿日期:2013-10-14 修回日期:2013-12-04 出版日期:2014-09-10 发布日期:2014-09-04
  • 通讯作者: 陈荣国 E-mail:qly1107@126.com;chenrongguo@beyondb.com.cn
  • 作者简介:

    作者简介:齐凌艳(1988-),女,安徽芜湖人,硕士生,研究方向为位置信息服务。E-mail: qly1107@126.com

  • 基金资助:
    国家自然科学基金项目(41001313);“863”国家高技术发展研究计划资助项目(2013AA12A204)

Research on the LBS Matching Based on Stay Point of the Semantic Trajectory

QI Lingyan1,2(), CHEN Rongguo1,*(), WEN Xin1,2   

  1. 1. State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China
    2. University of Chinese Academy of Sciences, Beijing 100049, China
  • Received:2013-10-14 Revised:2013-12-04 Online:2014-09-10 Published:2014-09-04
  • Contact: CHEN Rongguo E-mail:qly1107@126.com;chenrongguo@beyondb.com.cn
  • About author:

    *The author: CHEN Nan, E-mail:fjcn99@163.com

摘要:

在位置服务领域,用户轨迹在较大程度上体现了用户的日常行为模式,以及个人生活习惯等。利用GPS终端收集用户行为轨迹数据并加以挖掘分析,对于位置服务实现智能化推送有积极作用。用户行为轨迹的停留点分析是轨迹分析的常见手段之一。本研究首先将用户个性化信息,与轨迹点相关的地标名称等语义信息融入常规用户行为轨迹,形成“位置-语义”一体化的用户语义轨迹。然后,过滤原始轨迹错误点,提高数据精度,并在此基础上采用一种新的加权方法计算轨迹停留点坐标。最后,利用停留点坐标结合用户的兴趣、职业等个人信息,在扩充的POI信息库(包含营业时间、优惠信息等)中检索匹配,并智能化匹配出用户停留点周围的POI,主动向用户推送符合个人兴趣或职业需求的POI详情位置服务。

关键词: 语义轨迹, 停留点, 位置服务, POI

Abstract:

Location Based Service (LBS), with the support of GIS, is a thriving service for users related with coordinates received by wireless communication network or GPS. The trajectories composed with a set of received coordinates mainly express the character and habit of user’s behavior. Through analyzing and mining users’ trajectories, we will improve the efficiency of location based service. In this paper, firstly, the trajectories data including location coordinates and semantic fields are collected through GPS signal by the self-developed software installed in the terminal. The semantic fields contain the ID of user, current speed, nearby landmark and so on. Then the mistakes incorporated in raw trajectories due to the GPS instability should be filtered to enhance data accuracy. A method has been applied to filter the “jitter” points and to calculate the angle (angle threshold is 15°) and time interval (time threshold is 3s). Different from the conventional method that calculates mean value as the stay point’s coordinate directly, we divide the points in sub-trajectory into different groups based on semantic information. Afterwards, on the basis of the number of points in each group, we acquire weighted coordinate of the stay point. Finally, we match the stay points with POIs, which have ample information, like opening hours, special offers, etc., and then get a set of matched POIs around the stay point. In addition, through analyzing the interest and job of user, it could retrieve the more appropriate service and send it to user accordingly.

Key words: semantic trajectory, stay point, LBS, POI