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

Aug 4, 2026

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Smart cities enable urban transportation systems to become more sustainable and environmentally friendly, and effective traffic flow management is central to that transformation. The study investigates machine learning approaches for predicting traffic flow and using those predictions to support sustainable transportation strategies in smart cities. Data are drawn from the TRAFFIC CENSUS of the Hong Kong Transport Department. Anticipating traffic conditions can help governments reduce congestion, fuel consumption, and emissions while improving residents’ quality of life. The work compares different machine learning methodologies and discusses practical decision support for planners, policymakers, and urban designers to optimize traffic flow and strengthen transportation sustainability.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages22

Reading Time55 min

AccessFree to download

Introduction

Related Work and Background

Methodology and Machine Learning Approaches

Experimental Setup and Data Source

Results and Comparison

Implications for Transportation Policy and Planning

sustainability Article Towards Green Innovation in Smart Cities: Leveraging Traffic Flow Prediction with Machine Learning Algorithms for Sustainable Transportation Systems Xingyu Tao 1, Lan Cheng 2,*, Ruihan Zhang 3, W. K. Chan 4, Huang Chao 5 and Jing Qin 1 Citation: Tao, X.; Cheng, L.; Zhang, R.; Chan, W.K.; Chao, H.; Qin, J. Towards Green Innovation in Smart Cities: Leveraging Traffic Flow Prediction with Machine Learning Algorithms for Sustainable Transportation Systems. Sustainability 2024, 16, 251 . [https://doi.org/10.3390/su16010251](https://doi.org/10.3390/su16010251) [Academic Editors: Armando Carten](Academic Editors: Armando Carten)ì, Katarzyna Turo ´n and Feng Chen Received: 17 October 2023 Revised: 14 December 2023 Accepted: 19 December 2023 Published: 27 December 2023 Copyright: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) . 1 Centre for Smart Health, School of Nursing, The Hong Kong Polytechnic University, Hong Kong, China; [xingyu26.tao@polyu.edu.hk](xingyu26.tao@polyu.edu.hk) (X.T.); [harry.qin@polyu.edu.hk](harry.qin@polyu.edu.hk) (J.Q.) 2 Big Data Bio-Intelligence Laboratory, Big Data Institute, The Hong Kong University of Science and Technology, Hong Kong, China 3 The Department of Computing, The Hong Kong Polytechnic University, Hong Kong, China; [rzhangao@connect.ust.hk](rzhangao@connect.ust.hk) 4 The Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong, China; [wkinchan@outlook.com](wkinchan@outlook.com) 5 School of Arts and Design, Shenzhen University, Shenzhen 518060, China; [chao@szu.edu.cn](chao@szu.edu.cn) * Correspondence: [chenglan@ust.hk](chenglan@ust.hk) 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 1. Introduction Smart cities have emerged as a transformative solution for urban areas seeking to address the complex challenges of rapid urbanization, environmental sustainability, and efficient resource management. With the growing recognition of the impact of transportation on the environmental footprint of cities, there is an increasing need to develop sustainable transportation systems that decrease congestion, decrease fuel consump

FAQs

What problem does the paper address in smart cities?

It focuses on managing urban transportation sustainably, where traffic congestion and environmental impacts require more efficient control of traffic flow.

How is traffic flow prediction used to support sustainability goals?

Predicted traffic conditions enable proactive measures to alleviate congestion, reduce fuel consumption, minimize emissions, and improve quality of life.

What data source does the study use?

The study uses traffic data from the TRAFFIC CENSUS of the Hong Kong Transport Department.

Aug 4, 2026

Upload by Levi