Indoor mobility data encoding with TSTM-in: A topological-semantic trajectory model

IF 7.1 1区地球科学 Q1 ENVIRONMENTAL STUDIES

Computers Environment and Urban Systems Pub Date : 2024-04-25 DOI:10.1016/j.compenvurbsys.2024.102114

Jianxin Qin , Lu Wang , Tao Wu , Ye Li , Longgang Xiang , Yuanyuan Zhu

{"title":"Indoor mobility data encoding with TSTM-in: A topological-semantic trajectory model","authors":"Jianxin Qin , Lu Wang , Tao Wu , Ye Li , Longgang Xiang , Yuanyuan Zhu","doi":"10.1016/j.compenvurbsys.2024.102114","DOIUrl":null,"url":null,"abstract":"<div><p>The growing ubiquity of location/activity sensing technologies has created unprecedented opportunities for research on human spatiotemporal interaction behavior in mobile environments. However, existing studies of human mobility need to sufficiently account for the association of indoor scenes with the semantics of human behavior. This paper introduces TSTM-in, a trajectory model that combines trajectory data and indoor scenes using topological semantic modeling, semantic trajectory reconstruction, and trajectory queries. The model effectively manages indoor semantic trajectory data and extracts topological behavioral semantics by incorporating important points across a trajectory to reflect the semantics of key points connected to indoor corridors and regions. These topological semantics facilitate the creation of a flexible intersection-based indoor semantic trajectory reconstruction. Reconstructed semantic trajectories represent human mobility by integrating semantic data sets along the time axis. A case study with real-world trajectory queries from travelers demonstrates the model's effectiveness. TSTM-in realizes the association of indoor scenes with human behavior semantics, supporting the construction of mobile object management applications for indoor scenes and providing scientific and reasonable spatiotemporal semantic information description for location service-based intelligent cities.</p></div>","PeriodicalId":48241,"journal":{"name":"Computers Environment and Urban Systems","volume":"110 ","pages":"Article 102114"},"PeriodicalIF":7.1000,"publicationDate":"2024-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computers Environment and Urban Systems","FirstCategoryId":"89","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0198971524000437","RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENVIRONMENTAL STUDIES","Score":null,"Total":0}

引用次数: 0

Abstract

The growing ubiquity of location/activity sensing technologies has created unprecedented opportunities for research on human spatiotemporal interaction behavior in mobile environments. However, existing studies of human mobility need to sufficiently account for the association of indoor scenes with the semantics of human behavior. This paper introduces TSTM-in, a trajectory model that combines trajectory data and indoor scenes using topological semantic modeling, semantic trajectory reconstruction, and trajectory queries. The model effectively manages indoor semantic trajectory data and extracts topological behavioral semantics by incorporating important points across a trajectory to reflect the semantics of key points connected to indoor corridors and regions. These topological semantics facilitate the creation of a flexible intersection-based indoor semantic trajectory reconstruction. Reconstructed semantic trajectories represent human mobility by integrating semantic data sets along the time axis. A case study with real-world trajectory queries from travelers demonstrates the model's effectiveness. TSTM-in realizes the association of indoor scenes with human behavior semantics, supporting the construction of mobile object management applications for indoor scenes and providing scientific and reasonable spatiotemporal semantic information description for location service-based intelligent cities.

查看原文本刊更多论文

使用 TSTM-in 进行室内移动数据编码：拓扑语义轨迹模型

位置/活动感应技术的日益普及为移动环境中人类时空互动行为的研究创造了前所未有的机会。然而，现有的人类移动研究需要充分考虑室内场景与人类行为语义之间的关联。本文介绍的 TSTM-in 是一种轨迹模型，它通过拓扑语义建模、语义轨迹重建和轨迹查询将轨迹数据和室内场景结合起来。该模型可有效管理室内语义轨迹数据，并通过整合轨迹上的重要点来提取拓扑行为语义，以反映与室内走廊和区域相连的关键点的语义。这些拓扑语义有助于创建灵活的基于交叉点的室内语义轨迹重建。通过沿时间轴整合语义数据集，重建的语义轨迹代表了人类的移动性。一项针对旅行者真实轨迹查询的案例研究证明了该模型的有效性。TSTM-in 实现了室内场景与人类行为语义的关联，支持构建室内场景移动对象管理应用，为基于位置服务的智慧城市提供科学合理的时空语义信息描述。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Computers Environment and Urban Systems Multiple-

CiteScore

13.30

自引率

7.40%

发文量

111

审稿时长

32 days

期刊介绍： Computers, Environment and Urban Systemsis an interdisciplinary journal publishing cutting-edge and innovative computer-based research on environmental and urban systems, that privileges the geospatial perspective. The journal welcomes original high quality scholarship of a theoretical, applied or technological nature, and provides a stimulating presentation of perspectives, research developments, overviews of important new technologies and uses of major computational, information-based, and visualization innovations. Applied and theoretical contributions demonstrate the scope of computer-based analysis fostering a better understanding of environmental and urban systems, their spatial scope and their dynamics.