出租车轨迹数据的频繁轨迹识别

doi:10.13474/j.cnki.11-2246.2021.341

测绘通报 ›› 2021, Vol. 0 ›› Issue (11): 70-75.doi: 10.13474/j.cnki.11-2246.2021.341

出租车轨迹数据的频繁轨迹识别

邬群勇^1,2,3, 王祥健^1,2,3

1. 福州大学空间数据挖掘与信息共享教育部重点实验室, 福建福州 350108;
2. 数字中国研究院(福建), 福建福州 350003;
3. 卫星空间信息技术综合应用国家地方联合工程研究中心, 福建福州 350108

收稿日期:2020-11-12 修回日期:2021-04-01 出版日期:2021-11-25 发布日期:2021-12-02
作者简介:邬群勇(1973-),男,博士,研究员,研究方向为时空数据分析与地理信息服务。E-mail:qywu@fzu.edu.cn
基金资助:
国家自然科学基金（41471333）；中央引导地方科技发展专项（2017L3012）

Frequent trajectory recognition of taxi trajectory data

WU Qunyong^1,2,3, WANG Xiangjian^1,2,3

1. Key Lab of Spatial Data Mining and Information Sharing of Ministry of Education, Fuzhou University, Fuzhou 350108, China;
2. The Academy of Digital China(Fujian), Fuzhou 350003, China;
3. National & Local Joint Engineering Research Center of Satellite Geospatial Information Technology, Fuzhou 350108, China

Received:2020-11-12 Revised:2021-04-01 Online:2021-11-25 Published:2021-12-02

摘要/Abstract

摘要： 为识别城市交通中的频繁路径，本文提出了一种出租车轨迹数据的频繁轨迹识别方法。该方法首先对轨迹数据进行轨迹压缩，以降低计算复杂度；然后基于最长公共子序列和动态时间规整算法进行轨迹相似性度量计算，利用计算得到的轨迹间相似度生成距离矩阵；最后将生成的距离矩阵结合HDBSCAN算法进行聚类得到频繁轨迹。选取厦门岛内两个区域进行试验分析，结果表明，该方法能够识别出轨迹数据集中的频繁轨迹，进而得到城市区域之间通行的频繁路径，对道路规划、路径优化与推荐、交通治理等应用提供帮助。

关键词: 轨迹数据, 轨迹压缩, 轨迹相似度, 聚类簇, 频繁轨迹

Abstract: In order to identify the frequent paths in urban traffic, this paper proposes a method of frequent trajectory identification for taxi trajectory data. The method firstly compresses the trajectory data to reduce the computational complexity. Then calculates the trajectory similarity measure based on longest common subsequence and dynamic time warping algorithm, and generates a distance matrix by using the calculated similarity between trajectories. Finally, the generated distance matrix is clustered with HDBSCAN algorithm to get frequent trajectories. Two areas in Xiamen Island are selected for experimental analysis. The results show that the proposed method can identify the frequent trajectories in the trajectory data set and obtain the frequent paths between urban areas, which is helpful for road planning, path optimization and recommendation, traffic management and other applications.

Key words: trajectory data, trajectory compression, trajectory similarity, clustering group, frequent trajectory

中图分类号:

P208

邬群勇, 王祥健. 出租车轨迹数据的频繁轨迹识别[J]. 测绘通报, 2021, 0(11): 70-75.

WU Qunyong, WANG Xiangjian. Frequent trajectory recognition of taxi trajectory data[J]. Bulletin of Surveying and Mapping, 2021, 0(11): 70-75.

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出租车轨迹数据的频繁轨迹识别

Frequent trajectory recognition of taxi trajectory data

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