测绘通报 ›› 2020, Vol. 0 ›› Issue (5): 73-79.doi: 10.13474/j.cnki.11-2246.2020.0149

• 学术研究 • 上一篇    下一篇

用户GPS轨迹数据支持下的出行安全分析

张勇, 朱大明, 冯宇, 代德成   

  1. 昆明理工大学国土资源工程学院, 云南 昆明 650504
  • 收稿日期:2019-04-15 发布日期:2020-06-02
  • 通讯作者: 朱大明。E-mail:634617255@qq.com E-mail:634617255@qq.com
  • 作者简介:张勇(1995-),男,硕士生,研究方向为地理信息技术与开发应用。E-mail:1739694315@qq.com

A method for travel safety analysis supported by user GPS trajectory data

ZHANG Yong, ZHU Daming, FENG Yu, DAI Decheng   

  1. Kunming University of Science and Technology, Faculty of Land and Resources Engineering, Kunming 650504, China
  • Received:2019-04-15 Published:2020-06-02

摘要: 安全是每次出行的重要因素之一,传统方法大多基于人力或视频协同才能完成安全监控,缺少以轨迹数据度量出行的方法。定位和跟踪系统的技术进步,GPS卫星定位技术的不断发展与普及,使利用车载导航装置、手持GPS装置收集及使用GPS数据点成为可能。本文提出了一种基于用户GPS轨迹数据评价安全的方法,并以校区内宿舍与实验室间的轨迹数据为研究对象,验证了方法的有效性。研究表明:用户轨迹在离开安全轨迹处,获得较低的安全得分。反之,用户轨迹如果与安全轨迹极为相似,则获得很高的安全得分。该方法在检验用户轨迹是否与安全轨迹相似极为高效,同时以轨迹数据的形式反映用户的出行安全。

关键词: GPS轨迹, 出行安全, K-means聚类, 欧氏距离相似度, 距离安全得分

Abstract: Security is one of the important factors in every trip, traditional methods are mostly based on human or video collaboration to complete security monitoring, and there is no way to measure travel by trajectory data. With the technological improvement of positioning and tracking systems, the continuous development and popularization of GPS satellite positioning technology makes it possible to collect and use GPS data points on car navigation devices and handheld GPS devices.This paper proposes a method for evaluating security based on user GPS trajectory data, and uses trajectory data between dormitory and laboratory in the campus as the research object to verify the effectiveness of the method. Research shows that the user trajectory off the safety trajectory obtains a lower safety score. Conversely, if the user trajectory is very similar to the safety trajectory, a high safety score is obtained. This method is very efficient in verifying whether the user trajectory is similar to the safety trajectory or not, and at the same time, the form of the trajectory data reflects the user’s travel safety.

Key words: GPS trajectory, travel safety, K-means clustering, similarity of Euclidean distance, distance safety score

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