No Cover Image

Journal article 435 views 40 downloads

Predicting Next Useful Location With Context-Awareness: The State-Of-The-Art

Alireza Nezhadettehad Orcid Logo, Arkady Zaslavsky Orcid Logo, Rakib Abdur Orcid Logo, Siraj Shaikh Orcid Logo, Seng W. Loke Orcid Logo, GUANG-LI HUANG Orcid Logo, Alireza Hassani Orcid Logo

ACM Transactions on Intelligent Systems and Technology, Volume: 16, Issue: 5, Pages: 1 - 35

Swansea University Author: Siraj Shaikh Orcid Logo

  • 69739.VOR.pdf

    PDF | Version of Record

    © 2025 Copyright held by the owner/author(s). This work is licensed under a Creative Commons Attribution International 4.0.

    Download (58.09MB)

Check full text

DOI (Published version): 10.1145/3744653

Abstract

Predicting the future location of mobile objects reinforces location-aware services with proactive intelligence and helps businesses and decision-makers with better planning and near real-time scheduling in different applications such as traffic congestion control, location-aware advertisements, and...

Full description

Published in: ACM Transactions on Intelligent Systems and Technology
ISSN: 2157-6904 2157-6912
Published: Association for Computing Machinery (ACM) 2025
Online Access: Check full text

URI: https://cronfa.swan.ac.uk/Record/cronfa69739
first_indexed 2025-06-16T09:30:17Z
last_indexed 2025-11-08T06:13:48Z
id cronfa69739
recordtype SURis
fullrecord <?xml version="1.0"?><rfc1807><datestamp>2025-11-07T14:08:20.9416228</datestamp><bib-version>v2</bib-version><id>69739</id><entry>2025-06-16</entry><title>Predicting Next Useful Location With Context-Awareness: The State-Of-The-Art</title><swanseaauthors><author><sid>50117e8faac2d0937989e14847105704</sid><ORCID>0000-0002-0726-3319</ORCID><firstname>Siraj</firstname><surname>Shaikh</surname><name>Siraj Shaikh</name><active>true</active><ethesisStudent>false</ethesisStudent></author></swanseaauthors><date>2025-06-16</date><deptcode>MACS</deptcode><abstract>Predicting the future location of mobile objects reinforces location-aware services with proactive intelligence and helps businesses and decision-makers with better planning and near real-time scheduling in different applications such as traffic congestion control, location-aware advertisements, and monitoring public health and well-being. Recent developments in smartphone and location sensors technology and the prevalence of using location-based social networks alongside the improvements in artificial intelligence and machine learning techniques provide an excellent opportunity to exploit massive amounts of historical and real-time contextual information to recognise mobility patterns and achieve more accurate and intelligent predictions. This unique survey provides a comprehensive overview of the next useful location prediction problem with context-awareness and the related studies. First, we explain the concepts of context and context-awareness and define the next location prediction problem. Then we analyse more than thirty studies in this field concerning the prediction method, the challenges addressed, the datasets and metrics used for training and evaluating the model, and the types of context incorporated. Finally, we discuss the advantages and disadvantages of different approaches, focusing on the usefulness of the predicted location and identifying the open challenges and future work on this subject.</abstract><type>Journal Article</type><journal>ACM Transactions on Intelligent Systems and Technology</journal><volume>16</volume><journalNumber>5</journalNumber><paginationStart>1</paginationStart><paginationEnd>35</paginationEnd><publisher>Association for Computing Machinery (ACM)</publisher><placeOfPublication/><isbnPrint/><isbnElectronic/><issnPrint>2157-6904</issnPrint><issnElectronic>2157-6912</issnElectronic><keywords>Location prediction, Context-awareness, Location-awareness, Mobility prediction</keywords><publishedDay>1</publishedDay><publishedMonth>10</publishedMonth><publishedYear>2025</publishedYear><publishedDate>2025-10-01</publishedDate><doi>10.1145/3744653</doi><url/><notes/><college>COLLEGE NANME</college><department>Mathematics and Computer Science School</department><CollegeCode>COLLEGE CODE</CollegeCode><DepartmentCode>MACS</DepartmentCode><institution>Swansea University</institution><apcterm>Another institution paid the OA fee</apcterm><funders/><projectreference/><lastEdited>2025-11-07T14:08:20.9416228</lastEdited><Created>2025-06-16T10:25:50.6418745</Created><path><level id="1">Faculty of Science and Engineering</level><level id="2">School of Mathematics and Computer Science - Computer Science</level></path><authors><author><firstname>Alireza</firstname><surname>Nezhadettehad</surname><orcid>0000-0002-0275-4688</orcid><order>1</order></author><author><firstname>Arkady</firstname><surname>Zaslavsky</surname><orcid>0000-0003-1990-5734</orcid><order>2</order></author><author><firstname>Rakib</firstname><surname>Abdur</surname><orcid>0000-0001-5430-450x</orcid><order>3</order></author><author><firstname>Siraj</firstname><surname>Shaikh</surname><orcid>0000-0002-0726-3319</orcid><order>4</order></author><author><firstname>Seng W.</firstname><surname>Loke</surname><orcid>0000-0002-5339-9305</orcid><order>5</order></author><author><firstname>GUANG-LI</firstname><surname>HUANG</surname><orcid>0000-0001-8698-2946</orcid><order>6</order></author><author><firstname>Alireza</firstname><surname>Hassani</surname><orcid>0000-0002-4770-8183</orcid><order>7</order></author></authors><documents><document><filename>69739__35589__b8eddd73133b4f149a6c56d0e9f2db17.pdf</filename><originalFilename>69739.VOR.pdf</originalFilename><uploaded>2025-11-07T14:04:41.0342773</uploaded><type>Output</type><contentLength>60907935</contentLength><contentType>application/pdf</contentType><version>Version of Record</version><cronfaStatus>true</cronfaStatus><documentNotes>&#xA9; 2025 Copyright held by the owner/author(s). This work is licensed under a Creative Commons Attribution International 4.0.</documentNotes><copyrightCorrect>true</copyrightCorrect><language>eng</language><licence>https://creativecommons.org/licenses/by/4.0/</licence></document></documents><OutputDurs/></rfc1807>
spelling 2025-11-07T14:08:20.9416228 v2 69739 2025-06-16 Predicting Next Useful Location With Context-Awareness: The State-Of-The-Art 50117e8faac2d0937989e14847105704 0000-0002-0726-3319 Siraj Shaikh Siraj Shaikh true false 2025-06-16 MACS Predicting the future location of mobile objects reinforces location-aware services with proactive intelligence and helps businesses and decision-makers with better planning and near real-time scheduling in different applications such as traffic congestion control, location-aware advertisements, and monitoring public health and well-being. Recent developments in smartphone and location sensors technology and the prevalence of using location-based social networks alongside the improvements in artificial intelligence and machine learning techniques provide an excellent opportunity to exploit massive amounts of historical and real-time contextual information to recognise mobility patterns and achieve more accurate and intelligent predictions. This unique survey provides a comprehensive overview of the next useful location prediction problem with context-awareness and the related studies. First, we explain the concepts of context and context-awareness and define the next location prediction problem. Then we analyse more than thirty studies in this field concerning the prediction method, the challenges addressed, the datasets and metrics used for training and evaluating the model, and the types of context incorporated. Finally, we discuss the advantages and disadvantages of different approaches, focusing on the usefulness of the predicted location and identifying the open challenges and future work on this subject. Journal Article ACM Transactions on Intelligent Systems and Technology 16 5 1 35 Association for Computing Machinery (ACM) 2157-6904 2157-6912 Location prediction, Context-awareness, Location-awareness, Mobility prediction 1 10 2025 2025-10-01 10.1145/3744653 COLLEGE NANME Mathematics and Computer Science School COLLEGE CODE MACS Swansea University Another institution paid the OA fee 2025-11-07T14:08:20.9416228 2025-06-16T10:25:50.6418745 Faculty of Science and Engineering School of Mathematics and Computer Science - Computer Science Alireza Nezhadettehad 0000-0002-0275-4688 1 Arkady Zaslavsky 0000-0003-1990-5734 2 Rakib Abdur 0000-0001-5430-450x 3 Siraj Shaikh 0000-0002-0726-3319 4 Seng W. Loke 0000-0002-5339-9305 5 GUANG-LI HUANG 0000-0001-8698-2946 6 Alireza Hassani 0000-0002-4770-8183 7 69739__35589__b8eddd73133b4f149a6c56d0e9f2db17.pdf 69739.VOR.pdf 2025-11-07T14:04:41.0342773 Output 60907935 application/pdf Version of Record true © 2025 Copyright held by the owner/author(s). This work is licensed under a Creative Commons Attribution International 4.0. true eng https://creativecommons.org/licenses/by/4.0/
title Predicting Next Useful Location With Context-Awareness: The State-Of-The-Art
spellingShingle Predicting Next Useful Location With Context-Awareness: The State-Of-The-Art
Siraj Shaikh
title_short Predicting Next Useful Location With Context-Awareness: The State-Of-The-Art
title_full Predicting Next Useful Location With Context-Awareness: The State-Of-The-Art
title_fullStr Predicting Next Useful Location With Context-Awareness: The State-Of-The-Art
title_full_unstemmed Predicting Next Useful Location With Context-Awareness: The State-Of-The-Art
title_sort Predicting Next Useful Location With Context-Awareness: The State-Of-The-Art
author_id_str_mv 50117e8faac2d0937989e14847105704
author_id_fullname_str_mv 50117e8faac2d0937989e14847105704_***_Siraj Shaikh
author Siraj Shaikh
author2 Alireza Nezhadettehad
Arkady Zaslavsky
Rakib Abdur
Siraj Shaikh
Seng W. Loke
GUANG-LI HUANG
Alireza Hassani
format Journal article
container_title ACM Transactions on Intelligent Systems and Technology
container_volume 16
container_issue 5
container_start_page 1
publishDate 2025
institution Swansea University
issn 2157-6904
2157-6912
doi_str_mv 10.1145/3744653
publisher Association for Computing Machinery (ACM)
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Mathematics and Computer Science - Computer Science{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Mathematics and Computer Science - Computer Science
document_store_str 1
active_str 0
description Predicting the future location of mobile objects reinforces location-aware services with proactive intelligence and helps businesses and decision-makers with better planning and near real-time scheduling in different applications such as traffic congestion control, location-aware advertisements, and monitoring public health and well-being. Recent developments in smartphone and location sensors technology and the prevalence of using location-based social networks alongside the improvements in artificial intelligence and machine learning techniques provide an excellent opportunity to exploit massive amounts of historical and real-time contextual information to recognise mobility patterns and achieve more accurate and intelligent predictions. This unique survey provides a comprehensive overview of the next useful location prediction problem with context-awareness and the related studies. First, we explain the concepts of context and context-awareness and define the next location prediction problem. Then we analyse more than thirty studies in this field concerning the prediction method, the challenges addressed, the datasets and metrics used for training and evaluating the model, and the types of context incorporated. Finally, we discuss the advantages and disadvantages of different approaches, focusing on the usefulness of the predicted location and identifying the open challenges and future work on this subject.
published_date 2025-10-01T07:42:30Z
_version_ 1850743929426673664
score 11.08895

Similar Items