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.
Highlights
IntroductionThe initial concept of a smart city (SC) has been acknowledged as a framework that builds upon the advancements in the ICT (information and communication technology) field to address urbanization challenges
The initial concept of a smart city (SC) has been acknowledged as a framework that builds upon the advancements in the ICT field to address urbanization challenges
Experiments performed with real-world datasets collected from over 20,000 taxis and 1.7 million passengers in Singapore revealed that taxi service analyzer (TSA) detected passengers queuing with an accuracy of over 90%, with an insignificant energy overhead, and estimated wait time with less than a 15% error margin
Summary
The initial concept of a SC has been acknowledged as a framework that builds upon the advancements in the ICT (information and communication technology) field to address urbanization challenges. The development of frameworks for SCs has not fully matured to be able to take advantage of new and emerging data-driven technologies. The advancement of new technologies in big data, AI, machine learning, deep learning, and internet of things (IoT) will further shape the framework of a SC and revolutionize the different sectors in SCs [1,2]. Geoinformation and communication technology (GeoICT) [3] is another emerging field which is increasingly being utilized to foster urban sustainability and SCs. GeoICT has significant importance for the implementation of ICTs, involving geographic information science and systems in SCs to support analysis and decision-making
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