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Towards Green Innovation in Smart Cities: Leveraging Traffic Flow Prediction with Machine Learning Algorithms for Sustainable Transportation Systems

Author

Listed:
  • Xingyu Tao

    (Centre for Smart Health, School of Nursing, The Hong Kong Polytechnic University, Hong Kong, China)

  • Lan Cheng

    (Big Data Bio-Intelligence Laboratory, Big Data Institute, The Hong Kong University of Science and Technology, Hong Kong, China)

  • Ruihan Zhang

    (The Department of Computing, The Hong Kong Polytechnic University, Hong Kong, China)

  • W. K. Chan

    (The Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong, China)

  • Huang Chao

    (School of Arts and Design, Shenzhen University, Shenzhen 518060, China)

  • Jing Qin

    (Centre for Smart Health, School of Nursing, The Hong Kong Polytechnic University, Hong Kong, China)

Abstract

The emergence of smart cities has presented the prospect of transforming urban transportation systems into more sustainable and environmentally friendly entities. A pivotal facet of achieving this transformation lies in the efficient management of traffic flow. This paper explores the utilization of machine learning techniques for predicting traffic flow and its application in supporting sustainable transportation management strategies in smart cities based on data from the TRAFFIC CENSUS of the Hong Kong Transport Department. By analyzing anticipated traffic conditions, the government can implement proactive measures to alleviate congestion, reduce fuel consumption, minimize emissions, and ultimately improve quality of life for urban residents. This study proposes a way to develop traffic flow prediction methods with different methodologies in machine learning with a comparison with other results. This research aims to highlight the importance of leveraging machine learning technology in traffic flow prediction and its potential impact on sustainable transportation systems for the green innovation paradigm. The findings of this research have practical implications for transportation planners, policymakers, and urban designers. The predictive models demonstrated can support decision-making processes, enabling proactive measures to optimize traffic flow, reduce emissions, and improve the overall sustainability of transportation systems.

Suggested Citation

  • Xingyu Tao & Lan Cheng & Ruihan Zhang & W. K. Chan & Huang Chao & Jing Qin, 2023. "Towards Green Innovation in Smart Cities: Leveraging Traffic Flow Prediction with Machine Learning Algorithms for Sustainable Transportation Systems," Sustainability, MDPI, vol. 16(1), pages 1-22, December.
    • Handle: RePEc:gam:jsusta:v:16:y:2023:i:1:p:251-:d:1308395
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    References listed on IDEAS

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    3. Zawieska, Jakub & Pieriegud, Jana, 2018. "Smart city as a tool for sustainable mobility and transport decarbonisation," Transport Policy, Elsevier, vol. 63(C), pages 39-50.
    4. Hanifa Shah, 2023. "Beyond Smart: How ICT Is Enabling Sustainable Cities of the Future," Sustainability, MDPI, vol. 15(16), pages 1-22, August.
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