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

Xingyu Tao, Lan Cheng (), Ruihan Zhang, W. K. Chan, Huang Chao and Jing Qin
Additional contact information
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

Sustainability, 2023, vol. 16, issue 1, 1-22

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.

Keywords: smart city; transport management; machine learning technology; green innovation (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/2071-1050/16/1/251/pdf (application/pdf)
https://www.mdpi.com/2071-1050/16/1/251/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:16:y:2023:i:1:p:251-:d:1308395

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().