车辆轨迹数据处理、分析与应用综述

IF 28 1区 计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS ACM Computing Surveys Pub Date : 2025-03-06 DOI:10.1145/3715902
Chenxi Liu, Zhu Xiao, Wangchen Long, Tong Li, Hongbo Jiang, Keqin Li
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引用次数: 0

摘要

车辆在城市中行驶时,可扩展传感器会收集大量的车辆轨迹,为解决交通拥堵和公共安全等城市挑战提供了极好的机会。在本调查中,我们系统地回顾了车辆轨迹收集、预处理、分析和应用。首先,我们重点研究了车辆轨迹采集的标准技术和相应的数据集。接下来,我们介绍了车辆轨迹处理的最新进展。我们进一步讨论了使用车辆轨迹的个人出行行为和集体出行分析。由于私家车占城市车辆的大多数,并且是许多近期研究成果的基础,因此我们强调基于私家车轨迹数据的分析。然后,我们从计算车辆轨迹的角度编译了车辆轨迹增强应用程序。最后,对飞行器轨迹研究中尚未解决的问题进行了分析,并对未来的研究方向进行了展望。
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Vehicle Trajectory Data Processing, Analytics, and Applications: A Survey
Vehicles traveling through cities generate extensive vehicle trajectory collected by scalable sensors, providing excellent opportunities to address urban challenges such as traffic congestion and public safety. In this survey, we systematically review vehicle trajectory collection, preprocessing, analytics, and applications. First, we focus on the standard techniques for vehicle trajectory collection and corresponding datasets. Next, we introduce representative approaches for the latest advances in vehicle trajectory processing. We further discuss individual travel behavior and collective mobility analytics using vehicle trajectory. Since private cars constitute the majority of urban vehicles and form the basis for many recent research findings, we emphasize analytics based on private car trajectory data. We then compile vehicle trajectory-boosted applications from the perspective of computing vehicle trajectory. Finally, we go through unresolved problems with vehicle trajectory and outline potential future research directions.
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来源期刊
ACM Computing Surveys
ACM Computing Surveys 工程技术-计算机:理论方法
CiteScore
33.20
自引率
0.60%
发文量
372
审稿时长
12 months
期刊介绍: ACM Computing Surveys is an academic journal that focuses on publishing surveys and tutorials on various areas of computing research and practice. The journal aims to provide comprehensive and easily understandable articles that guide readers through the literature and help them understand topics outside their specialties. In terms of impact, CSUR has a high reputation with a 2022 Impact Factor of 16.6. It is ranked 3rd out of 111 journals in the field of Computer Science Theory & Methods. ACM Computing Surveys is indexed and abstracted in various services, including AI2 Semantic Scholar, Baidu, Clarivate/ISI: JCR, CNKI, DeepDyve, DTU, EBSCO: EDS/HOST, and IET Inspec, among others.
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