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Sustainability
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Sustainable Traffic Flow Management and Smart Transportation
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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: closed (10 September 2025) | Viewed by 2638

Special Issue Editors

Dr. Jinhao Liang

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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
Dr. Chaopeng Tan

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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
Dr. Jiwei Feng

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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
Prof. Dr. Jian Wu

E-Mail Website
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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Further information on MDPI's Special Issue policies can be found here.

Published Papers (4 papers)

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Research

35 pages, 2596 KB  
Article
Integrated Evaluation of C-ITS Services: Synergistic Effects of GLOSA and CACC on Traffic Efficiency and Sustainability
by Manuel Walch and Matthias Neubauer
Sustainability 2025, 17(19), 8855; https://doi.org/10.3390/su17198855 - 3 Oct 2025
Viewed by 299
Abstract
Cooperative Intelligent Transport Systems (C-ITS) have emerged as a key enabler of more efficient, safer, and environmentally sustainable road traffic by allowing vehicles and infrastructure to exchange information and coordinate behavior. To evaluate their benefits, impact assessment studies are essential. However, most existing [...] Read more.
Cooperative Intelligent Transport Systems (C-ITS) have emerged as a key enabler of more efficient, safer, and environmentally sustainable road traffic by allowing vehicles and infrastructure to exchange information and coordinate behavior. To evaluate their benefits, impact assessment studies are essential. However, most existing studies focus on individual C-ITS services in isolation, overlooking how combined deployments influence outcomes. This study addresses this gap by presenting the first systematic evaluation of individual and joint deployments of Cooperative Adaptive Cruise Control (CACC) and Green Light Optimal Speed Advisory (GLOSA) under diverse conditions. A dual-model simulation framework is applied, combining controlled artificial networks with calibrated real-world corridors in Upper Austria. This allows both statistical testing and validation of plausibility in real-world contexts. Key performance indicators include travel time and CO2 emissions, evaluated across varying lane configurations, numbers of traffic lights, demand levels, and equipment rates. The results demonstrate that C-ITS effectiveness is strongly context-dependent: while CACC generally provides larger efficiency gains, GLOSA yields consistent emission reductions, and the combined deployment offers conditional synergies but may also diminish benefits at high demand. The study contributes a guideline for selecting service configurations based on site conditions, thereby providing practical recommendations for future C-ITS rollouts. Full article
(This article belongs to the Special Issue Sustainable Traffic Flow Management and Smart Transportation)
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22 pages, 2963 KB  
Article
Classification Machine Learning Models for Enhancing the Sustainability of Postal System Modules Within the Smart Transportation Concept
by Milorad K. Banjanin, Mirko Stojčić, Đorđe Popović, Dejan Anđelković, Goran Jauševac and Maid Husić
Sustainability 2025, 17(19), 8718; https://doi.org/10.3390/su17198718 - 28 Sep 2025
Viewed by 389
Abstract
Postal traffic and transport face challenges related to the rapid growth of parcel volumes, increasing demands for sustainability, and the need for integration into the smart transportation concept. This study explores the application of machine learning (ML) models for the classification of postal [...] Read more.
Postal traffic and transport face challenges related to the rapid growth of parcel volumes, increasing demands for sustainability, and the need for integration into the smart transportation concept. This study explores the application of machine learning (ML) models for the classification of postal delivery times, with the aim of improving service efficiency and quality. As a case study, the Postal Center Zenica, one of the seven organizational units of the Public Enterprise “BH Pošta” in Bosnia and Herzegovina, was analyzed. The available dataset comprised 11,138 instances, which were cleaned and filtered, then expanded through two iterations of data augmentation using an autoencoder neural network. Five ML models, Random Forest, Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), k-Nearest Neighbors (kNN), and Multi-Layer Perceptron (MLP), were developed and compared, with hyperparameters optimized using the Bayesian method and evaluated through standard classification metrics. The results indicate that the data augmentation method significantly improves model performance, particularly in the classification of delayed shipments, with ensemble, especially Random Forest and XGBoost, emerging as the most robust solutions. Beyond contributions in the context of postal traffic and transport, the proposed methodological framework demonstrates interdisciplinary relevance, as it can also be applied in telecommunication traffic classes, where similar network dynamics require reliable predictive models. Full article
(This article belongs to the Special Issue Sustainable Traffic Flow Management and Smart Transportation)
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19 pages, 1383 KB  
Article
Make Train Stations More Respondent to User Needs: An Italian Case Study
by Cristina Pronello, Francesco Torre and Alessandra Boggio Marzet
Sustainability 2025, 17(17), 7838; https://doi.org/10.3390/su17177838 - 31 Aug 2025
Viewed by 552
Abstract
Within transport systems, train stations cover a primary role as places where access to different modes of transport must be realised effectively, providing a valuable opportunity to make rail services, public transport, and soft mobility more attractive. This research seeks to shed some [...] Read more.
Within transport systems, train stations cover a primary role as places where access to different modes of transport must be realised effectively, providing a valuable opportunity to make rail services, public transport, and soft mobility more attractive. This research seeks to shed some light on how Italian travellers perceive the quality of train stations, and to identify priorities for action in relation to design, building, and operation that might help revitalise their attractiveness. The methodology involved designing a questionnaire capable of identifying significant correlations between attitudinal and behavioural variables via an exploratory factor analysis, reaching around 400 respondents through a snowball sampling plan. The factor “sociality and daily life” showed the importance that people place on the vitality of urban places. Travellers also consider other factors, like the overall service quality, the cleanliness and safety of a train station, the walkability of connections within the node, and the possibility of reaching the station by bicycle. The profiling of respondents using a cluster analysis based on latent factors points to specific policies, showing how actions targeting stations can have positive effects on the use of rail transport and on the propensity towards intermodality and sustainable mobility. A safe, “living” place can mitigate the risk of social degradation, while promoting walking and cycling. Full article
(This article belongs to the Special Issue Sustainable Traffic Flow Management and Smart Transportation)
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22 pages, 7043 KB  
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 609
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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