MobilityDL:轨迹数据深度学习综述

IF 2.2 4区 计算机科学 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS Geoinformatica Pub Date : 2024-05-28 DOI:10.1007/s10707-024-00518-8
Anita Graser, Anahid Jalali, Jasmin Lampert, Axel Weißenfeld, Krzysztof Janowicz
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引用次数: 0

摘要

轨迹数据结合了时间序列、空间数据和(有时不合理的)运动行为的复杂性。随着数据可用性和计算能力的提高,轨迹数据深度学习也越来越受欢迎。本综述论文首次全面概述了针对轨迹数据的深度学习方法。我们确定了八个具体的移动性用例,并对其所使用的深度学习模型和训练数据进行了分析。除了对 2018 年以来的文献进行全面的定量回顾外,我们工作的主要贡献在于以数据为中心分析了该领域的最新工作,并将其置于移动数据连续体中,该连续体包括单个移动者的详细密集轨迹(准连续跟踪数据)、稀疏轨迹(如签到数据)和聚合轨迹(人群信息)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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MobilityDL: a review of deep learning from trajectory data

Trajectory data combines the complexities of time series, spatial data, and (sometimes irrational) movement behavior. As data availability and computing power have increased, so has the popularity of deep learning from trajectory data. This review paper provides the first comprehensive overview of deep learning approaches for trajectory data. We have identified eight specific mobility use cases which we analyze with regards to the deep learning models and the training data used. Besides a comprehensive quantitative review of the literature since 2018, the main contribution of our work is the data-centric analysis of recent work in this field, placing it along the mobility data continuum which ranges from detailed dense trajectories of individual movers (quasi-continuous tracking data), to sparse trajectories (such as check-in data), and aggregated trajectories (crowd information).

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来源期刊
Geoinformatica
Geoinformatica 地学-计算机:信息系统
CiteScore
5.60
自引率
10.00%
发文量
25
审稿时长
6 months
期刊介绍: GeoInformatica is located at the confluence of two rapidly advancing domains: Computer Science and Geographic Information Science; nowadays, Earth studies use more and more sophisticated computing theory and tools, and computer processing of Earth observations through Geographic Information Systems (GIS) attracts a great deal of attention from governmental, industrial and research worlds. This journal aims to promote the most innovative results coming from the research in the field of computer science applied to geographic information systems. Thus, GeoInformatica provides an effective forum for disseminating original and fundamental research and experience in the rapidly advancing area of the use of computer science for spatial studies.
期刊最新文献
LENS: label sparsity-tolerant adversarial learning on spatial deceptive reviews A case study of spatiotemporal forecasting techniques for weather forecasting CLMTR: a generic framework for contrastive multi-modal trajectory representation learning Periodicity aware spatial-temporal adaptive hypergraph neural network for traffic forecasting ICN: Interactive convolutional network for forecasting travel demand of shared micromobility
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