Emerging Technologies for Smart Cities’ Transportation: Geo-Information, Data Analytics and Machine Learning Approaches
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, there...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-01-01
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| Series: | ISPRS International Journal of Geo-Information |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2220-9964/11/2/85 |
| _version_ | 1797479442935709696 |
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| author | Kenneth Li-Minn Ang Jasmine Kah Phooi Seng Ericmoore Ngharamike Gerald K. Ijemaru |
| author_facet | Kenneth Li-Minn Ang Jasmine Kah Phooi Seng Ericmoore Ngharamike Gerald K. Ijemaru |
| author_sort | Kenneth Li-Minn Ang |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T21:46:44Z |
| format | Article |
| id | doaj.art-5947c4ec949c4a1281f921f3666550e2 |
| institution | Directory Open Access Journal |
| issn | 2220-9964 |
| language | English |
| last_indexed | 2024-03-09T21:46:44Z |
| publishDate | 2022-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | ISPRS International Journal of Geo-Information |
| spelling | doaj.art-5947c4ec949c4a1281f921f3666550e22023-11-23T20:15:30ZengMDPI AGISPRS International Journal of Geo-Information2220-99642022-01-011128510.3390/ijgi11020085Emerging Technologies for Smart Cities’ Transportation: Geo-Information, Data Analytics and Machine Learning ApproachesKenneth Li-Minn Ang0Jasmine Kah Phooi Seng1Ericmoore Ngharamike2Gerald K. Ijemaru3School of Science, Technology and Engineering, University of the Sunshine Coast, Petrie, QLD 4502, AustraliaSchool of AI and Advanced Computing, Xian Jiaotong Liverpool University, Suzhou 215123, ChinaDepartment of Computer Science, Federal University Oye-Ekiti, Oye Ekiti 371104, Ekiti, NigeriaSchool of Science, Technology and Engineering, University of the Sunshine Coast, Petrie, QLD 4502, AustraliaWith 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.https://www.mdpi.com/2220-9964/11/2/85geo-informationtransportationsmart citiesmachine learningdata analyticsbig data |
| spellingShingle | Kenneth Li-Minn Ang Jasmine Kah Phooi Seng Ericmoore Ngharamike Gerald K. Ijemaru Emerging Technologies for Smart Cities’ Transportation: Geo-Information, Data Analytics and Machine Learning Approaches ISPRS International Journal of Geo-Information geo-information transportation smart cities machine learning data analytics big data |
| title | Emerging Technologies for Smart Cities’ Transportation: Geo-Information, Data Analytics and Machine Learning Approaches |
| title_full | Emerging Technologies for Smart Cities’ Transportation: Geo-Information, Data Analytics and Machine Learning Approaches |
| title_fullStr | Emerging Technologies for Smart Cities’ Transportation: Geo-Information, Data Analytics and Machine Learning Approaches |
| title_full_unstemmed | Emerging Technologies for Smart Cities’ Transportation: Geo-Information, Data Analytics and Machine Learning Approaches |
| title_short | Emerging Technologies for Smart Cities’ Transportation: Geo-Information, Data Analytics and Machine Learning Approaches |
| title_sort | emerging technologies for smart cities transportation geo information data analytics and machine learning approaches |
| topic | geo-information transportation smart cities machine learning data analytics big data |
| url | https://www.mdpi.com/2220-9964/11/2/85 |
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