research-article

Similar Trajectory Search with Spatio-Temporal Deep Representation Learning

Authors:

David Alexander Tedjopurnomo,

Farhana Choudhury,

A. K. QinAuthors Info & Claims

ACM Transactions on Intelligent Systems and Technology (TIST), Volume 12, Issue 6

Article No.: 77, Pages 1 - 26

https://doi.org/10.1145/3466687

Published: 11 December 2021 Publication History

Abstract

Similar trajectory search is a crucial task that facilitates many downstream spatial data analytic applications. Despite its importance, many of the current literature focus solely on the trajectory’s spatial similarity while neglecting the temporal information. Additionally, the few papers that use both the spatial and temporal features based their approach on a traditional point-to-point comparison. These methods model the importance of the spatial and temporal aspect of the data with only a single, pre-defined balancing factor for all trajectories, even though the relative spatial and temporal balance can change from trajectory to trajectory. In this article, we propose the first spatio-temporal, deep-representation-learning-based approach to similar trajectory search. Experiments show that utilizing both features offers significant improvements over existing point-to-point comparison and deep-representation-learning approach. We also show that our deep neural network approach is faster and performs more consistently compared to the point-to-point comparison approaches.

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Cited By

Zou XYan YHao XHu YWen HLiu EZhang JLi YLi TZheng YLiang Y(2025)Deep learning for cross-domain data fusion in urban computing: Taxonomy, advances, and outlookInformation Fusion10.1016/j.inffus.2024.102606113(102606)Online publication date: Jan-2025
https://doi.org/10.1016/j.inffus.2024.102606
Si JYang JXiang YWang HLi LZhang RTu BChen X(2024)TrajBERT: BERT-Based Trajectory Recovery With Spatial-Temporal Refinement for Implicit Sparse TrajectoriesIEEE Transactions on Mobile Computing10.1109/TMC.2023.329711523:5(4849-4860)Online publication date: May-2024
https://doi.org/10.1109/TMC.2023.3297115
Wang CHuang JWang YLin ZJin XJin XWeng DWu Y(2024)A Deep Spatiotemporal Trajectory Representation Learning Framework for ClusteringIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2024.335033925:7(7687-7700)Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1109/TITS.2024.3350339
Show More Cited By

Index Terms

Similar Trajectory Search with Spatio-Temporal Deep Representation Learning
1. Computing methodologies
  1. Artificial intelligence
    1. Knowledge representation and reasoning
      1. Spatial and physical reasoning
      2. Temporal reasoning

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Published In

cover image ACM Transactions on Intelligent Systems and Technology

ACM Transactions on Intelligent Systems and Technology Volume 12, Issue 6

December 2021

356 pages

ISSN:2157-6904

EISSN:2157-6912

DOI:10.1145/3501281

Editor:
Huan Liu
Arizona State University, USA

Issue’s Table of Contents

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 11 December 2021

Accepted: 01 May 2021

Revised: 01 March 2021

Received: 01 November 2020

Published in TIST Volume 12, Issue 6

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ARC
Singtel Cognitive and Artificial Intelligence Lab for Enterprises (SCALE@NTU)
Singapore Telecommunications Limited (Singtel) and Nanyang Technological University (NTU)
Singapore Government through the Industry Alignment Fund - Industry Collaboration Projects
Tier-1 project

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Cited By

Zou XYan YHao XHu YWen HLiu EZhang JLi YLi TZheng YLiang Y(2025)Deep learning for cross-domain data fusion in urban computing: Taxonomy, advances, and outlookInformation Fusion10.1016/j.inffus.2024.102606113(102606)Online publication date: Jan-2025
https://doi.org/10.1016/j.inffus.2024.102606
Si JYang JXiang YWang HLi LZhang RTu BChen X(2024)TrajBERT: BERT-Based Trajectory Recovery With Spatial-Temporal Refinement for Implicit Sparse TrajectoriesIEEE Transactions on Mobile Computing10.1109/TMC.2023.329711523:5(4849-4860)Online publication date: May-2024
https://doi.org/10.1109/TMC.2023.3297115
Wang CHuang JWang YLin ZJin XJin XWeng DWu Y(2024)A Deep Spatiotemporal Trajectory Representation Learning Framework for ClusteringIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2024.335033925:7(7687-7700)Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1109/TITS.2024.3350339
Liu ZKang CXing X(2023)Querying Similar Multi-Dimensional Time Series with a Spatial DatabaseISPRS International Journal of Geo-Information10.3390/ijgi1204017912:4(179)Online publication date: 21-Apr-2023
https://doi.org/10.3390/ijgi12040179
Liu PWu DShen ZWang H(2023)Trajectory privacy data publishing scheme based on local optimisation and R-treeConnection Science10.1080/09540091.2023.220388035:1Online publication date: 30-Apr-2023
https://doi.org/10.1080/09540091.2023.2203880
Yu XZhu SRen Y(2023)Continuous trajectory similarity search with result diversificationFuture Generation Computer Systems10.1016/j.future.2023.02.011143:C(392-400)Online publication date: 1-Jun-2023
https://dl.acm.org/doi/10.1016/j.future.2023.02.011

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