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Sustainable Intelligent Transport Systems: AI-Driven Multi-Modal Fusion for Green Development
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Sustainable Intelligent Transport Systems: AI-Driven Multi-Modal Fusion for Green Development

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 3782

Special Issue Editors

Dr. Xinqiang Chen

E-Mail Website
Guest Editor
Insitute of Logistics Science and Engineering, Shanghai Maritime University, Shanghai 201306, China
Interests: intelligent transportation; engineering; smart ships; intelligent navigation; traffic flow analysis
Special Issues, Collections and Topics in MDPI journals
Dr. Qiang Luo

E-Mail Website
Guest Editor
School of Civil Engineering and Transportation, Guangzhou University, Guangzhou 510006, China
Interests: road safety
Dr. Gen Li

E-Mail Website
Guest Editor
College of Automobile and Traffic Engineering, Nanjing Forestry University, Nanjing 210037, China
Interests: road safety; heavy trucks; intelligent transportation systems; data mining; sustainable transportation; auto driving; driving behavior
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Modern transportation systems, encompassing road, maritime, aviation, and logistics networks, serve as critical arteries for global economic activities and societal well-being. The rapid evolution of large-scale data collection technologies (e.g., IoT sensors, mobile trajectories) and breakthroughs in artificial intelligence (AI) have opened new frontiers for addressing long-standing challenges in traffic flow optimization, intermodal coordination, and sustainability.

With the acceleration of global urbanization and the intensification of the climate crisis, transportation systems are facing multidimensional challenges: low efficiency of multimodal collaboration, high carbon emissions and energy waste, uncertainty regarding user behaviors, aging transportation infrastructure, and driving safety issues for the elderly. This Special Issue focuses on the innovative applications of data-driven and artificial intelligence technology in multimodal transportation systems, aiming to promote breakthroughs in the following areas:

  • Theory and methods of multimodal traffic data fusion;
  • Green transportation energy management and optimization;
  • AI-powered dynamic decision-making models for transportation systems;
  • The systemic integration of autonomous driving and shared mobility;
  • Low-carbon operation and maintenance technology for transportation infrastructure;
  • Digital transformation and upgrades of transportation infrastructure;
  • Evaluation and optimization of traffic sign and line setting plans;
  • Driving safety protection technology and strategies for the elderly;
  • Multi-modal carbon emission trading mechanisms and green incentive strategies;
  • Coordinated scheduling of the electric truck charging network and power grid;
  • Modeling of the spatiotemporal distribution of traffic noise and air pollution exposure;
  • Energy consumption optimization and path planning for autonomous ships and port systems;
  • Maritime traffic perception: fusion analysis of multi-source sensory data;
  • Optimization of ship paths in uncertain environments.

Dr. Xinqiang Chen
Dr. Qiang Luo
Dr. Gen Li
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 transport
  • green transport
  • sustainable transportation systems

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Published Papers (5 papers)

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Research

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23 pages, 1202 KB  
Article
Coordinated Multi-Intersection Traffic Signal Control Using a Policy-Regulated Deep Q-Network
by Lin Ma, Yan Liu, Yang Liu, Changxi Ma and Shanpu Wang
Sustainability 2026, 18(3), 1510; https://doi.org/10.3390/su18031510 - 2 Feb 2026
Viewed by 612
Abstract
Coordinated control across multiple signalized intersections is essential for mitigating congestion propagation in urban road networks. However, existing DQN-based approaches often suffer from unstable action switching, limited interpretability, and insufficient capability to model spatial spillback between adjacent intersections. To address these limitations, this [...] Read more.
Coordinated control across multiple signalized intersections is essential for mitigating congestion propagation in urban road networks. However, existing DQN-based approaches often suffer from unstable action switching, limited interpretability, and insufficient capability to model spatial spillback between adjacent intersections. To address these limitations, this study proposes a Policy-Regulated and Aligned Deep Q-Network (PRA-DQN) for cooperative multi-intersection signal control. A differentiable policy function is introduced and explicitly trained to align with the optimal Q-value-derived target distribution, yielding more stable and interpretable policy behavior. In addition, a cooperative reward structure integrating local delay, movement pressure, and upstream–downstream interactions enables agents to simultaneously optimize local efficiency and regional coordination. A parameter-sharing multi-agent framework further enhances scalability and learning consistency across intersections. Simulation experiments conducted on a 2 × 2 SUMO grid show that PRA-DQN consistently outperforms fixed-time, classical DQN, distributed DQN, and pressure/wave-based baselines. Compared with fixed-time control, PRA-DQN reduces maximum queue length by 21.17%, average queue length by 18.75%, and average waiting time by 17.71%. Moreover, relative to classical DQN coordination, PRA-DQN achieves an additional 7.53% reduction in average waiting time. These results confirm the effectiveness and superiority of the proposed method in suppressing congestion propagation and improving network-level traffic performance. The proposed PRA-DQN provides a practical and scalable basis for real-time deployment of coordinated signal control and can be readily extended to larger networks and time-varying demand conditions. Full article
(This article belongs to the Special Issue Sustainable Intelligent Transport Systems: AI-Driven Multi-Modal Fusion for Green Development)
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16 pages, 1962 KB  
Article
Hierarchical Analysis for Construction Risk Factors of Highway Engineering Based on DEMATEL-MMDE-ISM Method
by Peng Zhang, Yandong He, Yibo Zhang, Rong Li and Biao Wu
Sustainability 2026, 18(1), 116; https://doi.org/10.3390/su18010116 - 22 Dec 2025
Viewed by 600
Abstract
To effectively mitigate risks in highway construction and thereby ensure the sustainable development of the transportation sector, this study identifies 27 risk factors across five dimensions—human–machine–environment–process–management—through a combination of literature review, construction accident case analyses, and expert interviews. The Decision-Making Trial and Evaluation [...] Read more.
To effectively mitigate risks in highway construction and thereby ensure the sustainable development of the transportation sector, this study identifies 27 risk factors across five dimensions—human–machine–environment–process–management—through a combination of literature review, construction accident case analyses, and expert interviews. The Decision-Making Trial and Evaluation Laboratory (DEMATEL) method, combined with the Maximum Mean Deviation Entropy (MMDE) approach for threshold determination, quantifies centrality and causality of these factors. An Interpretive Structural Modeling (ISM) is employed to construct a multi-level hierarchical framework. The research reveals that highway construction safety risks follow a seven-tier structure: “risk characterization-process assurance-source governance-driven”. Safety education and regulatory systems serve as fundamental drivers, while hazard identification and mitigation, extreme weather response protocols, and equipment compliance form critical safeguard mechanisms. Building on this framework, the study proposes a risk control pathway of “source governance–process interruption–terminal response”, offering practical recommendations for safety management and providing new perspectives for engineering risk assessment and method optimization. Full article
(This article belongs to the Special Issue Sustainable Intelligent Transport Systems: AI-Driven Multi-Modal Fusion for Green Development)
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26 pages, 7801 KB  
Article
Enhancing Sustainable Intelligent Transportation Systems Through Lightweight Monocular Depth Estimation Based on Volume Density
by Xianfeng Tan, Chengcheng Wang, Ziyu Zhang, Zhendong Ping, Jieying Pan, Hao Shan, Ruikai Li, Meng Chi and Zhiyong Cui
Sustainability 2025, 17(24), 11271; https://doi.org/10.3390/su172411271 - 16 Dec 2025
Cited by 1 | Viewed by 603
Abstract
Depth estimation is a critical enabling technology for sustainable intelligent transportation systems (ITSs), as it supports essential functions such as obstacle detection, navigation, and traffic management. However, existing Neural Radiance Field (NeRF)-based monocular depth estimation methods often suffer from high computational costs and [...] Read more.
Depth estimation is a critical enabling technology for sustainable intelligent transportation systems (ITSs), as it supports essential functions such as obstacle detection, navigation, and traffic management. However, existing Neural Radiance Field (NeRF)-based monocular depth estimation methods often suffer from high computational costs and poor performance in occluded regions, limiting their applicability in real-world, resource-constrained environments. To address these challenges, this paper proposes a lightweight monocular depth estimation framework that integrates a novel capacity redistribution strategy and an adaptive occlusion-aware training mechanism. By shifting computational load from resource-intensive multi-layer perceptrons (MLPs) to efficient separable convolutional encoder–decoder networks, our method significantly reduces memory usage to 234 MB while maintaining competitive accuracy. Furthermore, a divide-and-conquer training strategy explicitly handles occluded regions, improving reconstruction quality in complex urban scenarios. Experimental evaluations on the KITTI and V2X-Sim datasets demonstrate that our approach not only achieves superior depth estimation performance but also supports real-time operation on edge devices. This work contributes to the sustainable development of ITS by offering a practical, efficient, and scalable solution for environmental perception, with potential benefits for energy efficiency, system affordability, and large-scale deployment. Full article
(This article belongs to the Special Issue Sustainable Intelligent Transport Systems: AI-Driven Multi-Modal Fusion for Green Development)
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19 pages, 2396 KB  
Article
A Multi-Objective Reinforcement Learning Framework for Energy-Efficient Electric Bus Operations
by Huan Liu, Hengyi Qiu, Wanming Lu and Xiaonian Shan
Sustainability 2025, 17(23), 10695; https://doi.org/10.3390/su172310695 - 28 Nov 2025
Cited by 1 | Viewed by 722
Abstract
In urban arterials, buses face dual constraints from signal-controlled intersections and bus stop dwell demands, and frequent start–stop cycles result in reduced operational efficiency and elevated energy consumption. To address this critical challenge, a sustainable eco-driving strategy integrating offline and online Reinforcement Learning [...] Read more.
In urban arterials, buses face dual constraints from signal-controlled intersections and bus stop dwell demands, and frequent start–stop cycles result in reduced operational efficiency and elevated energy consumption. To address this critical challenge, a sustainable eco-driving strategy integrating offline and online Reinforcement Learning (RL) is proposed in this study. Leveraging real-world trajectory data from a 15.47 km route with 31 stops, the energy consumption characteristics of electric buses under the combined effects of stops and intersections are systematically analyzed, and high energy consumption scenarios are precisely identified. An initial energy saving strategy is first trained using offline RL, and subsequently subjected to online optimization in a vehicle–infrastructure cooperative simulation environment that incorporates three typical stop configurations. The soft actor-critic algorithm is employed to reconcile the dual goals of energy efficiency and ride comfort. Simulation results reveal a significant improvement with the proposed strategy, achieving an 11.2% reduction in energy consumption and a 37.7% decrease in travel time compared to the Krauss benchmark model. This study confirms the effectiveness of RL in boosting the operational sustainability of public transport systems, offering a scalable technical framework to promote the development of green urban mobility. The research findings provide theoretical support and practical references for the large-scale promotion and engineering application of energy saving autonomous driving technology for electric buses. Full article
(This article belongs to the Special Issue Sustainable Intelligent Transport Systems: AI-Driven Multi-Modal Fusion for Green Development)
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Review

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29 pages, 2174 KB  
Review
Energy Management Technologies for All-Electric Ships: A Comprehensive Review for Sustainable Maritime Transport
by Lyu Xing, Yiqun Wang, Han Zhang, Guangnian Xiao, Xinqiang Chen, Qingjun Li, Lan Mu and Li Cai
Sustainability 2026, 18(8), 3778; https://doi.org/10.3390/su18083778 - 10 Apr 2026
Viewed by 539
Abstract
To systematically review the research progress, methodological frameworks, and application characteristics of energy management technologies for All-Electric Ships (AES), this review provides a comprehensive and critical survey of studies published over the past two decades, following the technical trajectory of multi-energy coupling–multi-objective optimization–engineering-oriented [...] Read more.
To systematically review the research progress, methodological frameworks, and application characteristics of energy management technologies for All-Electric Ships (AES), this review provides a comprehensive and critical survey of studies published over the past two decades, following the technical trajectory of multi-energy coupling–multi-objective optimization–engineering-oriented operation. Based on a structured analysis of representative literature, the review first elucidates the overall architecture and operational characteristics of AES energy systems from a system-level perspective, highlighting their core advantages as “mobile microgrids” in terms of multi-energy coordination and dispatch flexibility. On this basis, a structured classification framework for energy management strategies is established, and the theoretical foundations, applicable scenarios, and engineering feasibility of rule-based, optimization-based, uncertainty-aware, and intelligent/data-driven approaches are comparatively reviewed and discussed. Furthermore, focusing on key research themes—including multi-energy system optimization, ship–port–microgrid coordinated operation, battery safety and lifetime-oriented management, and real-time energy management strategies—the review synthesizes the main findings and engineering validation progress reported in recent studies. The analysis indicates that, with the integration of fuel cells, renewable energy sources, and Hybrid Energy Storage Systems (HESS), energy management for AES has evolved from a single power allocation problem into a system-level optimization challenge involving multiple time scales, multiple objectives, and diverse sources of uncertainty. Optimization-based and Model Predictive Control (MPC) methods have shown promising performance in many simulation and pilot-scale studies for improving energy efficiency and emission performance, while robust optimization and data-driven approaches offer useful support for enhancing operational resilience, prediction capability, and decision quality under complex and uncertain conditions. These advances collectively contribute to the environmental, economic, and operational sustainability of maritime transport by reducing greenhouse gas emissions, extending equipment lifetime, and enabling efficient integration of renewable energy sources. At the same time, the current literature still reveals important limitations related to model fidelity, data availability, validation maturity, and the gap between methodological sophistication and practical deployment. Overall, an increasingly structured but still evolving research framework has emerged in this field. Future research should further strengthen ship–port–microgrid coordinated energy management frameworks, develop system-level optimization methods that integrate safety constraints and uncertainty, and advance intelligent Energy Management Systems (EMS) oriented toward sustainable zero-carbon shipping objectives. Full article
(This article belongs to the Special Issue Sustainable Intelligent Transport Systems: AI-Driven Multi-Modal Fusion for Green Development)
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