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Emerging Technologies for Smart Cities Transportation - Geo-Information, Data Analytics and Machine Learning Approaches13.94 MB
Published VersionCC BY V4.0, Open Access
Journal article
Emerging Technologies for Smart Cities’ Transportation: Geo-Information, Data Analytics and Machine Learning Approaches
ISPRS International Journal of Geo-Information, Vol.11(2), pp.1-45
2022
Abstract
With the recent increase in urban drift, which has led to an unprecedented surge in urban population, the smart city (SC) transportation industry faces a myriad of challenges, including the development of efficient strategies to utilize available infrastructures and minimize traffic. There is, therefore, the need to devise efficient transportation strategies to tackle the issues affecting the SC transportation industry. This paper reviews the state-of-the-art for SC transportation techniques and approaches. The paper gives a comprehensive review and discussion with a focus on emerging technologies from several information and data-driven perspectives including (1) geoinformation approaches; (2) data analytics approaches; (3) machine learning approaches; (4) integrated deep learning approaches; (5) artificial intelligence (AI) approaches. The paper contains core discussions on the impacts of geo-information on SC transportation, data-driven transportation and big data technology, machine learning approaches for SC transportation, innovative artificial intelligence (AI) approaches for SC transportation, and recent trends revealed by using integrated deep learning towards SC transportation. This survey paper aimed to give useful insights to researchers regarding the roles that data-driven approaches can be utilized for in smart cities (SCs) and transportation. An objective of this paper was to acquaint researchers with the recent trends and emerging technologies for SC transportation applications, and to give useful insights to researchers on how these technologies can be exploited for SC transportation strategies. To the best of our knowledge, this is the first comprehensive review that examines the impacts of the various five driving technological forces—geoinformation, data-driven and big data technology, machine learning, integrated deep learning, and AI—in the context of SC transportation applications.
Details
- Title
- Emerging Technologies for Smart Cities’ Transportation: Geo-Information, Data Analytics and Machine Learning Approaches
- Authors
- Kenneth Ang (Author) - University of the Sunshine Coast, Queensland, School of Science, Technology and EngineeringJasmine Kah Phooi Seng (Author) - Charles Sturt UniversityEricmoore T Ngharamike (Author) - Federal University Oye EkitiGerald Ijemaru (Author) - University of the Sunshine Coast, Queensland, School of Science, Technology and Engineering
- Publication details
- ISPRS International Journal of Geo-Information, Vol.11(2), pp.1-45
- Publisher
- MDPI AG
- Date published
- 2022
- DOI
- 10.3390/ijgi11020085
- ISSN
- 2220-9964
- Copyright note
- Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
- Organisation Unit
- University of the Sunshine Coast, Queensland; School of Science, Technology and Engineering; Engage Research Lab
- Language
- English
- Record Identifier
- 99605108702621
- Output Type
- Journal article
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- Collaboration types
- Domestic collaboration
- International collaboration
- Web Of Science research areas
- Computer Science, Information Systems
- Geography, Physical
- Remote Sensing
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