地球信息科学学报 ›› 2022, Vol. 24 ›› Issue (3): 458-468.doi: 10.12082/dqxxkx.2022.210408
朱秋圳1,2,3(), 邬群勇1,2,3,*(
), 姚铖鑫1,2,3, 孙豪宇1,2,3
收稿日期:
2021-07-18
修回日期:
2021-08-30
出版日期:
2022-03-25
发布日期:
2022-05-25
通讯作者:
*邬群勇(1973— ),男,山东诸城人,研究员,主要从事时空数据挖掘和地理信息服务研究。 E-mail: qywu@fzu.edu.cn作者简介:
朱秋圳(1996— ),男,福建平和人,硕士生,主要从事时空数据挖掘研究。E-mail: zqiiuz@163.com
基金资助:
ZHU Qiuzhen1,2,3(), WU Qunyong1,2,3,*(
), YAO Chengxin1,2,3, SUN Haoyu1,2,3
Received:
2021-07-18
Revised:
2021-08-30
Online:
2022-03-25
Published:
2022-05-25
Contact:
WU Qunyong
Supported by:
摘要:
浮动车轨迹数据已逐渐成为城市交通状态识别的主要数据源之一,但是现有基于浮动车轨迹数据的交通状态识别中多数是应用高精度或是多源轨迹数据。针对稀疏轨迹数据在城市交通状态识别中存在识别精度不高的问题,本文提出一种结合戴维森堡丁指数(DBI)和轨迹相似性度量的动态交通状态划分方法。首先,对轨迹数据和路网数据进行预处理并且建立不同时间片的路段轨迹集合;接着,依据轨迹速度-空间相似性,利用戴维森堡丁指数动态地扩展轨迹的空间维度,并根据轨迹相似性度量方法构建最佳车辆队列;然后,将前后不同的车辆队列进行二次处理,连接组成交通流簇;最后,基于模糊C均值聚类方法将交通流进行划分,实现路段交通状态的识别。采用厦门市厦禾路、湖滨西路和湖滨南路交叉路段上的真实出租车轨迹数据进行测试,结果表明,本文所提方法保证了车辆队列速度分布与原始轨迹速度分布基本一致,相比对比方法Kmeans++和ST-DBSCAN,本文方法均方根误差平均下降了18.77%和21.22%,并且在不同的实验路段表现更加稳定,可有效、可靠地运用稀疏轨迹数据识别城市交通状态,进而实现城市交通状态的精细分析。
朱秋圳, 邬群勇, 姚铖鑫, 孙豪宇. 基于DBI和稀疏轨迹数据的交通状态精细划分与识别[J]. 地球信息科学学报, 2022, 24(3): 458-468.DOI:10.12082/dqxxkx.2022.210408
ZHU Qiuzhen, WU Qunyong, YAO Chengxin, SUN Haoyu. Fine Classification and Identification of Traffic States based on DBI and Sparse Trajectory Data[J]. Journal of Geo-information Science, 2022, 24(3): 458-468.DOI:10.12082/dqxxkx.2022.210408
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