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Sustainable Traffic Flow Management and Smart Transportation

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Sustainable Transportation".

Deadline for manuscript submissions: 10 September 2025 | Viewed by 489

Special Issue Editors


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Guest Editor
Department of Civil & Environmental Engineering, National University of Singapore, 1 Engineering Drive 2, E1A 08-20, Singapore 117576, Singapore
Interests: connected and autonomous vehicles; intelligent transportation system; intelligent vehicle control

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Guest Editor
Department of Transport and Planning, Delft University of Technology, Gebouw 23, Stevinweg 1, 2628 CN Delft, The Netherlands
Interests: intelligent transportation systems; traffic modeling and control with connected vehicles; privacy-preserving traffic control
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
School of Mechanical and Automotive Engineering, Liaocheng University, Liaocheng 252000, China
Interests: vehicle dynamics and chassis dynamic coordination control; automated vehicles and intelligent control; human–machine collaborative control

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Guest Editor
School of Mechanical and Automotive Engineering, Liaocheng University, Liaocheng 252000, China
Interests: vehicle dynamics and chassis dynamic coordination control; automated vehicles and intelligent control; human–machine collaborative control
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue focuses on innovative approaches and technologies for sustainable traffic flow management and smart transportation systems. As urbanization accelerates and vehicle ownership increases, traditional traffic management strategies are becoming insufficient in addressing congestion, pollution, and safety concerns.

The contributions to this Special Issue explore the integration of advanced technologies, such as Internet of Things (IoT), artificial intelligence (AI), and big data analytics, to enhance traffic management efficiency. Key topics include the development of intelligent transportation systems (ITSs) that leverage real-time data to optimize traffic flow, reduce emissions, and improve overall road safety. Additionally, we emphasize the role of sustainable practices in traffic management, highlighting strategies for promoting public transportation, non-motorized transport, and eco-friendly vehicle technologies.

Submissions may address theoretical frameworks, empirical studies, and case studies that demonstrate the effectiveness of smart transportation solutions. We encourage interdisciplinary research that bridges engineering, urban planning, environmental science, and social behavior to create a holistic understanding of sustainable traffic flow management. Through this Special Issue, we aim to provide a platform for researchers and practitioners to share their findings, foster collaboration, and contribute to the ongoing discourse on sustainable urban mobility. Topics of interest include but are not limited to the following:

  • AI-driven traffic signal optimization;
  • Impact of connected vehicles on traffic flow;
  • Sustainable public transportation models;
  • Advanced autonomous driving technology;
  • Human–vehicle cooperation control in mixed traffic;
  • Data-driven approaches for traffic incident management;
  • Environmental impact assessment of traffic management strategies;
  • User behavior and smart transportation systems;
  • Resilient transportation systems under climate change;
  • Collaborative positioning of intelligent connected vehicles in complex urban environments.

Dr. Jinhao Liang
Dr. Chaopeng Tan
Dr. Jiwei Feng
Prof. Dr. Jian Wu
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sustainability is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • sustainable traffic management
  • smart transportation systems
  • advanced autonomous driving technologies
  • intelligent transportation systems (ITSs)
  • urban mobility

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Published Papers (1 paper)

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Research

22 pages, 7043 KiB  
Article
A Study on the Speed Decision Control of Agricultural Vehicles in a Collaborative Multi-Machine Operation Scenario
by Guangfei Xu, Jiwei Feng, Quanjin Wang, Dongxin Xu, Jingbin Sun, Meizhou Chen and Jian Wu
Sustainability 2025, 17(10), 4326; https://doi.org/10.3390/su17104326 - 9 May 2025
Viewed by 232
Abstract
The driving speed of autonomous agricultural vehicles is influenced by surrounding cooperative vehicles during cooperative operations, leading to challenges in simultaneously optimizing operational efficiency, energy consumption, safety, and driving smoothness. This bottleneck hinders the development of autonomous cooperative systems. To address this, we [...] Read more.
The driving speed of autonomous agricultural vehicles is influenced by surrounding cooperative vehicles during cooperative operations, leading to challenges in simultaneously optimizing operational efficiency, energy consumption, safety, and driving smoothness. This bottleneck hinders the development of autonomous cooperative systems. To address this, we propose a hierarchical speed decision control framework. The speed decision layer employs a maximum entropy-constrained proximal policy optimization (DMEPPO) reinforcement learning method, incorporating operational efficiency, energy consumption, safety, and smoothness as reward metrics to determine the optimal speed target. The speed control layer utilizes a Linear Matrix Inequality (LMI)-based robust control method for precise speed tracking. The experimental results demonstrate that the proposed DMEPPO achieved convergence after 2000 iterations and better learning performance, while the LMI-based controller achieved robust and responsive tracking. This architecture provides a theoretical foundation for speed decision control in agricultural vehicle cooperation scenarios. By considering aspects of speed decision-making control such as energy conservation, good solutions can be provided for the sustainable development of agriculture. Full article
(This article belongs to the Special Issue Sustainable Traffic Flow Management and Smart Transportation)
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