Emerging Technologies for Smart Cities’ Transportation

Geo-Information, Data Analytics and Machine Learning Approaches

Bibliographic Data

ID22032201
AuthorsKenneth Li-Minn Ang (University of the Sunshine Coast, corresponding author), Jasmine Kah Phooi Seng (0000-0002-8071-9044, Queensland University of Technology), Ericmoore Ngharamike (0000-0002-3572-9956, Federal University Oye Ekiti), Gerald K Ijemaru (0000-0001-5146-6829, University of the Sunshine Coast)
Year2022
Volume11
Issue2
Pages85
Publication date2022-01-24
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueISPRS International Journal of Geo-Information (JOURNAL)
Journal identifiersISSN: 2220-9964 • E-ISSN: 2220-9964
PublisherMDPI AG (PUBLISHER • IT)
DOI10.3390/ijgi11020085
OpenAlexW4210761929
LanguageEN
Citations received2
References cited187

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

Advanced Traffic Management System · Analytics · Big data · Data mining · Data science · Emerging technologies · Geographic information system · Intelligent transportation system · Transport engineering · Computer Science · Engineering · Human Mobility and Location-Based Analysis · Traffic Prediction and Management Techniques · Transportation Planning and Optimization · Artificial Intelligence

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