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Sustainability
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Intelligent Transportation Systems for Sustainable Transportation Management
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Intelligent Transportation Systems for Sustainable Transportation Management

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

Deadline for manuscript submissions: 30 September 2026 | Viewed by 2396

Special Issue Editors

Dr. Bo Yu

E-Mail Website
Guest Editor
College of Transportation, Tongji University, Shanghai 201804, China
Interests: intelligent transportation infrastructures; eco-driving behavior modeling; connected and autonomous vehicles
Special Issues, Collections and Topics in MDPI journals
Dr. Yuan Wang

E-Mail Website
Guest Editor
College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310014, China
Interests: intelligent transportation safety; automotive electronics; energy management of new energy vehicles
Dr. You Kong

E-Mail Website
Guest Editor
College of Transport and Communication, Shanghai Maritime University, Shanghai 201306, China
Interests: autonomous vehicles; parking management; intelligent transportation system
Special Issues, Collections and Topics in MDPI journals
Dr. Zeyang Cheng

E-Mail Website
Guest Editor
School of Automotive and Transportation Engineering, Hefei University of Technology, Hefei 230009, China
Interests: traffic safety; self-driving; intelligent transportation systems

Special Issue Information

Dear Colleagues,

We are pleased to announce a new Special Issue entitled “Intelligent Transportation Systems for Sustainable Transportation Management” in the journal Sustainability. This issue aims to explore the integration of advanced technologies such as connected and automated vehicles, big data analytics, machine learning, electrification, and shared mobility into intelligent transportation systems (ITS) to enhance both safety and environmental sustainability.

With the rapid development of ITS, new opportunities and challenges arise in managing transportation systems more efficiently and responsibly. This Special Issue invites original research and review articles that address key aspects of sustainable transportation management, including, but not limited to, the following: eco-driving, transportation electrification, shared mobility optimization, human factors in ITS, safety performance of advanced driver-assistance systems (ADAS), and behavioral shifts toward sustainable travel modes. We also encourage submissions that leverage emerging methods such as deep learning, edge computing, and multi-source data fusion to propose innovative solutions for reducing emissions, improving energy efficiency, and enhancing overall system resilience.

We welcome interdisciplinary contributions that electronic engineering, data science, environmental science, and social sciences to promote a safer, greener, and more sustainable future in transportation. This issue provides a platform for researchers and practitioners to share insights, methodologies, and policy recommendations that support the transition to smarter and more sustainable mobility systems.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

  • Safety performance and risk analysis in intelligent transportation systems;
  • Driver behavior modeling in automated and connected environments;
  • Eco-driving and energy-efficient traffic management strategies;
  • Integration and optimization of electric vehicles into urban transport networks;
  • Advanced driver-assistance systems (ADAS) and their impact on traffic safety and efficiency;
  • Machine learning and AI applications for sustainable transportation planning and management;
  • Multi-sensor data fusion and real-time traffic monitoring using IoT and edge computing;
  • Behavioral shifts and public acceptance of sustainable transportation modes;
  • Life-cycle assessment and environmental impact analysis of intelligent transport technologies.

We encourage contributions that combine technical innovation with socio-economic and environmental perspectives to advance the transition toward safe, efficient, and low-carbon mobility systems.

We look forward to receiving your contributions.

Dr. Bo Yu
Dr. Yuan Wang
Dr. You Kong
Dr. Zeyang Cheng
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 250 words) can be sent to the Editorial Office for assessment.

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

  • intelligent transportation systems (ITS)
  • sustainable transportation management
  • connected and automated vehicles (CAVs)
  • eco-driving and energy efficiency
  • human factors in automated driving
  • edge computing & IoT for traffic management
  • environmental impact assessment
  • real-time traffic optimization
  • big data analytics in mobility
  • artificial intelligence in transportation management

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (3 papers)

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Research

27 pages, 10340 KB  
Article
A Coordinated Operation Framework for Mobile Charging Robots and Fixed Charging Piles: Layout Design and Performance Analysis
by You Kong, Congwen Deng, Jiaheng Zhang and Ruijie Li
Sustainability 2026, 18(4), 2009; https://doi.org/10.3390/su18042009 - 15 Feb 2026
Viewed by 352
Abstract
The rapid growth of electric vehicles (EVs) is intensifying charging demand in space-constrained parking facilities, where fixed charging piles (FCPs) are often underutilized due to parking–charging coupling and stall blocking. This study develops a coordinated planning framework for a hybrid charging system that [...] Read more.
The rapid growth of electric vehicles (EVs) is intensifying charging demand in space-constrained parking facilities, where fixed charging piles (FCPs) are often underutilized due to parking–charging coupling and stall blocking. This study develops a coordinated planning framework for a hybrid charging system that integrates FCPs and mobile charging robots (MCRs). Two optimization models—operator profit maximization and social welfare maximization—are formulated to jointly determine the capacity configuration (numbers of FCPs and MCRs) and the spatial layout of FCPs and MCR base stations, subject to a queueing-theory-based waiting-time constraint. A nested heuristic solution method combining particle swarm optimization (PSO) and K-means++ is designed for tractable computation. Numerical experiments on a representative parking facility demonstrate a clear complementarity between fixed and mobile chargers: FCPs serve baseload demand economically, while MCRs provide flexible capacity that reduces average waiting time and mitigates congestion. The results further quantify the divergence between private and social objectives; when robot costs are reduced, the social-welfare model deploys approximately 35% more robots than the profit-maximizing solution to reduce user time losses. By improving charger utilization, the proposed hybrid planning approach enhances resource efficiency and supports sustainable EV charging infrastructure in dense urban parking facilities. Full article
(This article belongs to the Special Issue Intelligent Transportation Systems for Sustainable Transportation Management)
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23 pages, 2592 KB  
Article
Reinforcement Learning-Based Vehicle Control in Mixed-Traffic Environments with Driving Style-Aware Trajectory Prediction
by Xiaopeng Zhang, Lin Wang, Yipeng Zhang and Zewei Feng
Sustainability 2025, 17(24), 10889; https://doi.org/10.3390/su172410889 - 5 Dec 2025
Viewed by 846
Abstract
The heterogeneity of human driving styles in mixed-traffic environments manifests as divergent decision-making behaviors in complex scenarios like highway merging. By accurately recognizing these driving styles and predicting corresponding trajectories, autonomous vehicles can enhance safety, improve traffic efficiency, and concurrently achieve fuel savings [...] Read more.
The heterogeneity of human driving styles in mixed-traffic environments manifests as divergent decision-making behaviors in complex scenarios like highway merging. By accurately recognizing these driving styles and predicting corresponding trajectories, autonomous vehicles can enhance safety, improve traffic efficiency, and concurrently achieve fuel savings in highway merging scenarios. This paper proposes a novel framework wherein a clustering algorithm first establishes statistical priors of driving styles. These priors are then integrated into a Model Predictive Control (MPC) model that leverages Bayesian inference to generate a probability-aware trajectory prediction. Finally, this predicted trajectory is embedded as a component of the state input to a reinforcement learning agent, which is trained using an Actor–Critic architecture to learn the optimal control policy. Experimental results validate the significant superiority of the proposed framework. Under the most challenging high-density traffic scenarios, our method boosts the evaluation reward by 11.26% and the average speed by 10.08% compared to the baseline Multi-Agent Proximal Policy Optimization (MAPPO) algorithm. This advantage also persists in low-density scenarios, where a steady 10.60% improvement in evaluation reward is achieved. These findings confirm that the proposed integrated approach provides an effective decision-making solution for autonomous vehicles, capable of substantially enhancing interaction safety and traffic efficiency in emerging mixed-traffic environments. Full article
(This article belongs to the Special Issue Intelligent Transportation Systems for Sustainable Transportation Management)
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28 pages, 4465 KB  
Article
Analysis and Prediction of Factors Influencing Fatigue Driving in Freight Vehicles Based on Causal Analysis and GBDT Model
by Yi Li, Zhitian Wang and Ying Yang
Sustainability 2025, 17(23), 10687; https://doi.org/10.3390/su172310687 - 28 Nov 2025
Viewed by 672
Abstract
Fatigue driving of freight vehicles is a major threat to transport safety, often causing heavy casualties and property losses. However, existing studies only focus on superficial correlations between fatigue driving and influencing factors, failing to reveal intrinsic causal mechanisms, which limits practical guidance [...] Read more.
Fatigue driving of freight vehicles is a major threat to transport safety, often causing heavy casualties and property losses. However, existing studies only focus on superficial correlations between fatigue driving and influencing factors, failing to reveal intrinsic causal mechanisms, which limits practical guidance for prevention. To address this gap, this study, focusing on safety performance analysis in intelligent transportation systems and machine learning applications for sustainable transport management, uses monitoring data of “two types of passenger vehicles and one type of hazardous materials transport vehicle” in Shanghai. It identifies causal relationships between fatigue driving and 19 key factors (vehicle speed, driving time period, etc.) via a causal inference framework. Results show that 10 factors (including driving during specific periods) positively affect fatigue driving, while 9 factors (including vehicle speed) have negative effects. A Causal-GBDT Hybrid Model is built by weighting causal core factors into XGBoost (1.7.6) and CatBoost (1.2). Results show causal weights raise XGBoost accuracy from 90% to 93% and CatBoost from 89% to 94%. This clarifies fatigue triggers, provides technical support for targeted prevention, and advances machine learning in freight safety risk management. The research results can provide technical support for the development of real-time fatigue warning systems for freight vehicle and traffic safety management policies, contributing to the sustainable improvement of road transport safety. Full article
(This article belongs to the Special Issue Intelligent Transportation Systems for Sustainable Transportation Management)
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