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Sustainable Intelligent Transportation: Cooperative Systems and Vehicle Automation

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

Deadline for manuscript submissions: 30 June 2025 | Viewed by 9043

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Special Issue Editors

Prof. Dr. Jia Hu

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Guest Editor

College of Transportation Engineering, Tongji University, Shanghai 201804, China
Interests: vehicle–infrastructure cooperative decision making

Dr. Haoran Wang

E-Mail Website
Guest Editor

College of Transportation Engineering, Tongji University, Shanghai 201804, China
Interests: vehicle cooperative automation

Dr. Jintao Lai

E-Mail Website
Guest Editor

College of Transportation Engineering, Tongji University, Shanghai 201804, China
Interests: AV testing and simulation

Special Issue Information

Dear Colleagues,

Sustainability invites researchers and experts in the field of transportation to contribute to a Special Issue titled "Sustainable Intelligent Transportation: Cooperative Systems and Vehicle Automation." This Special Issue aims to explore the intersection of intelligent transportation, cooperative systems, and vehicle automation, with a strong focus on sustainability.

Intelligent transportation systems have revolutionized the way we move and interact with our transportation networks. These systems, driven by advanced technologies such as information technology, control technology, and computer technology, have significantly improved efficiency, safety, and reliability. However, with the pressing need to address environmental concerns and enhance sustainability in transportation, the concept of sustainable intelligent transportation has gained paramount importance.

Cooperative systems, which involve vehicles communicating and collaborating with each other and with infrastructure, hold great promise in improving transportation performance, reducing congestion, and enhancing safety. Additionally, vehicle automation technologies, including autonomous vehicles and driver assistance systems, have the potential to revolutionize transportation by reducing emissions, improving energy efficiency, and enhancing traffic flow.

This Special Issue aims to bring together researchers, practitioners, and experts from various disciplines to explore the latest advancements, challenges, and opportunities in sustainable intelligent transportation, with a specific focus on cooperative systems and vehicle automation. We welcome original research articles, reviews, and case studies covering a wide range of topics related to this theme, including but not limited to:

Cooperative systems for intelligent transportation;
Vehicle automation technologies and their impact on sustainability;
Intelligent transportation infrastructure and communication systems;
Energy-efficient routing and traffic management;
Sustainable mobility solutions and shared transportation services;
Intelligent transportation systems for smart cities;
Environmental impact assessment of intelligent transportation systems;
Policy and regulatory frameworks for sustainable intelligent transportation;
Human factors and user acceptance of cooperative systems and vehicle automation;
Safety and reliability of autonomous and cooperative vehicles.

We invite authors to submit their high-quality contributions to this Special issue. Manuscripts should adhere to the journal's guidelines for authors and will undergo a rigorous peer-review process. Accepted papers will be published in Sustainability, a reputable publication known for its commitment to advancing the field of transportation and sustainability.

Join us in shaping the future of sustainable intelligent transportation through cooperative systems and vehicle automation. We look forward to receiving your valuable contributions.

Prof. Dr. Jia Hu
Dr. Haoran Wang
Dr. Jintao Lai
Guest Editor

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 transportation
cooperative system
vehicle automation
driver assistance
eco-driving
energy management

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Published Papers (8 papers)

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Research

25 pages, 7180 KiB

Open AccessArticle

A Novel Max-Pressure-Driven Integrated Ramp Metering and Variable Speed Limit Control for Port Motorways

by Weiqi Yue, Hang Yang, Yibing Wang, Yusheng Zhou, Guiyun Liu and Pengjun Zheng

Sustainability 2025, 17(12), 5592; https://doi.org/10.3390/su17125592 - 18 Jun 2025

Viewed by 184

Abstract

In recent years, congestion on port motorways has become increasingly frequent, significantly constraining transportation efficiency and contributing to higher pollution emissions. This paper proposes a novel max-pressure-driven integrated control (IFC-MP) for port motorways, inspired by the max pressure (MP) concept, which continuously adjusts the weights of ramp metering (RM) and the variable speed limit (VSL) based on pressure feedback from the on-ramp and upstream, assigning greater control weight to the side with higher pressure. A queue management mechanism is incorporated to prevent on-ramp overflow. The effectiveness of IFC-MP is verified in SUMO, filling the gap where the previous integrated control methods for port motorways lacked micro-simulation validation. The results show that IFC-MP enhances bottleneck throughput by approximately 7% compared to the no-control case, optimizes the total time spent (TTS) by 26–27%, and improves total pollutant emissions (TPEs) by about 11%. Compared to strategies that use only RM and VSL control, or activate VSL control only after RM reaches its lower bound, the time–space distribution of speed under IFC-MP is more uniform, with smaller fluctuations in bottleneck occupancy. Additionally, IFC-MP maintains relatively stable performance under varying compliance levels. Overall, the IFC-MP is an effective method for alleviating congestion on port motorways, excelling in optimizing both traffic efficiency and pollutant emissions. Full article

(This article belongs to the Special Issue Sustainable Intelligent Transportation: Cooperative Systems and Vehicle Automation)

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30 pages, 11900 KiB

Open AccessArticle

Enhancing Mixed Traffic Stability with TD3-Driven Bilateral Control in Autonomous Vehicle Chains

by Kan Liu, Pengpeng Jiao, Weiqi Hong and Yue Chen

Sustainability 2025, 17(11), 4790; https://doi.org/10.3390/su17114790 - 23 May 2025

Viewed by 433

Abstract

This study presents a TD3-driven Bilateral Control Model (TD3-BCM) aimed at improving the stability of mixed traffic flows in autonomous vehicle (AV) chains. By integrating deep reinforcement learning, TD3-BCM optimizes control strategies to reduce traffic oscillations, smooth speed and acceleration fluctuations, and enhance overall system performance. Stability analysis shows that TD3-BCM effectively suppresses traffic fluctuations, with system stability improving from 1.132 to 1.182 as AV penetration increases. At an AV penetration rate of 40%, TD3-BCM surpasses both Cooperative Adaptive Cruise Control (CACC) and traditional Bilateral Control Model (BCM) approaches in terms of traffic efficiency, safety, and energy use—raising trailing vehicle speed by 12.6%, shortening average headway by 19.0%, increasing Time-to-Collision (TTC) by 87.3%, and lowering fuel consumption by 14.8%. When AV penetration reaches 70%, fuel savings rise to 19.7%, accompanied by further improvements in both traffic stability and safety. TD3-BCM provides a scalable and sustainable solution for intelligent transportation systems, particularly in high-penetration AV environments, by significantly enhancing stability, operational efficiency, and road safety. Full article

(This article belongs to the Special Issue Sustainable Intelligent Transportation: Cooperative Systems and Vehicle Automation)

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17 pages, 5707 KiB

Open AccessArticle

AI-Enabled Digital Twin Framework for Safe and Sustainable Intelligent Transportation

by Keke Long, Chengyuan Ma, Hangyu Li, Zheng Li, Heye Huang, Haotian Shi, Zilin Huang, Zihao Sheng, Lei Shi, Pei Li, Sikai Chen and Xiaopeng Li

Sustainability 2025, 17(10), 4391; https://doi.org/10.3390/su17104391 - 12 May 2025

Viewed by 765

Abstract

This study proposes an AI-powered digital twin (DT) platform designed to support real-time traffic risk prediction, decision-making, and sustainable mobility in smart cities. The system integrates multi-source data—including static infrastructure maps, historical traffic records, telematics data, and camera feeds—into a unified cyber–physical platform. AI models are employed for data fusion, anomaly detection, and predictive analytics. In particular, the platform incorporates telematics–video fusion for enhanced trajectory accuracy and LiDAR–camera fusion for high-definition work-zone mapping. These capabilities support dynamic safety heatmaps, congestion forecasts, and scenario-based decision support. A pilot deployment on Madison’s Flex Lane corridor demonstrates real-time data processing, traffic incident reconstruction, crash-risk forecasting, and eco-driving control using a validated Vehicle-in-the-Loop setup. The modular API design enables integration with existing Advanced Traffic Management Systems (ATMSs) and supports scalable implementation. By combining predictive analytics with real-world deployment, this research offers a practical approach to improving urban traffic safety, resilience, and sustainability. Full article

(This article belongs to the Special Issue Sustainable Intelligent Transportation: Cooperative Systems and Vehicle Automation)

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21 pages, 9309 KiB

Open AccessArticle

Efficient Roadside Vehicle Line-Pressing Identification in Intelligent Transportation Systems with Mask-Guided Attention

by Yuxiang Qin, Xinzhou Qi, Ruochen Hao, Tuo Sun and Jun Song

Sustainability 2025, 17(9), 3845; https://doi.org/10.3390/su17093845 - 24 Apr 2025

Viewed by 334

Abstract

Vehicle line-pressing identification from a roadside perspective is a challenging task in intelligent transportation systems. Factors such as vehicle pose and environmental lighting significantly affect identification performance, and the high cost of data collection further exacerbates the problem. Existing methods struggle to achieve robust results across different scenarios. To improve the robustness of roadside vehicle line-pressing identification, we propose an efficient method. First, we construct the first large-scale vehicle line-pressing dataset based on roadside cameras (VLPI-RC). Second, we design an end-to-end convolutional neural network that integrates vehicle and lane line mask features, incorporating a mask-guided attention module to focus on key regions relevant to line-pressing events. Finally, we introduce a binary balanced contrastive loss (BBCL) to improve the model’s ability to generate more discriminative features, addressing the class imbalance issue in binary classification tasks. Experimental results demonstrate that our method achieves 98.65% accuracy and 96.34% F1 on the VLPI-RC dataset. Moreover, when integrated into the YOLOv5 object detection framework, it attains an identification speed of 108.29 FPS. These results highlight the effectiveness of our approach in accurately and efficiently detecting vehicle line-pressing behaviors. Full article

(This article belongs to the Special Issue Sustainable Intelligent Transportation: Cooperative Systems and Vehicle Automation)

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16 pages, 1195 KiB

Open AccessArticle

Carbon Emission Reduction Assessment of Ships in the Grand Canal Network Based on Synthetic Weighting and Matter-Element Extension Model

by Zhengchun Sun, Sudong Xu and Jun Jiang

Sustainability 2025, 17(1), 349; https://doi.org/10.3390/su17010349 - 5 Jan 2025

Viewed by 1344

Abstract

Vessel traffic is an important source of global greenhouse gas emissions. The carbon emissions from ships in the canal network are directly linked to the environmental performance of China’s inland waterway transportation, contributing to the achievement of global carbon reduction goals. Therefore, systematically assessing the carbon emission reduction levels of ships in canal networks is essential to provide a robust foundation for developing more scientific and feasible emission reduction strategies. To address the limitations of current evaluations—which often focus on a single dimension and lack an objective, quantitative representation of the mechanisms driving carbon emission and their synergistic effects—this study took a comprehensive approach. First, considering the factors influencing ship carbon emissions and emission reduction strategies, an evaluation index system was developed. This system included 6 first-level indexes and 22 s-level indexes, covering aspects such as energy utilization, technical equipment, and economic benefits. Second, a novel combination of methods was used to construct an evaluation model. Qualitative weights, determined through the interval binary semantic method, were integrated with quantitative weights calculated using the CRITIC method. These were then combined and assigned using a game-theory-based comprehensive assignment method. The resulting evaluation model, built upon the theory of matter-element topology, represents a significant methodological innovation. Finally, the evaluation method was applied to the empirical analysis of ships operating in Jiangsu section of the Beijing–Hangzhou Grand Canal. This application demonstrated the model’s specificity and feasibility. The study’s findings provide valuable insights for improving carbon emission reduction levels for inland ships and advancing the sustainable development of the shipping industry. Full article

(This article belongs to the Special Issue Sustainable Intelligent Transportation: Cooperative Systems and Vehicle Automation)

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18 pages, 2193 KiB

Open AccessArticle

Evaluation of Autonomous Driving Safety by Operational Design Domains (ODD) in Mixed Traffic

by Hoseon Kim, Jieun Ko, Cheol Oh and Seoungbum Kim

Sustainability 2024, 16(22), 9672; https://doi.org/10.3390/su16229672 - 6 Nov 2024

Cited by 1 | Viewed by 1676

Abstract

This study derived effective driving behavior indicators to assess the driving safety of autonomous vehicles (AV). A variety of operation design domains (ODD) in urban road networks, which include intersections, illegal parking, bus stop, bicycle lanes, and pedestrian crossings, were taken into consideration in traffic simulation analyses. Both longitudinal and interaction driving indicators were investigated to identify the driving performance of AVs in terms of traffic safety in mixed traffic stream based on simulation experiments. As a result of identifying the appropriate evaluation indicator, time-varying stochastic volatility (VF) headway time was selected as a representative evaluation indicator for left turn and straight through signalized intersections among ODDs related to intersection types. VF headway time is suitable for evaluating driving ability by measuring the variation in driving safety in terms of interaction with the leading vehicle. In addition to ODDs associated with intersection type, U-turns, additional lane segments, illegal parking, bus stops, and merging lane have common characteristics that increase the likelihood of interactions with neighboring vehicles. The VF headway time for these ODDs was derived as driving safety in terms of interaction between vehicles. The results of this study would be valuable in establishing a guideline for driving performance evaluation of AVs. The study found that unsignalized left turns, signalized right turns, and roundabouts had the highest risk scores of 0.554, 0.525, and 0.501, respectively, indicating these as the most vulnerable ODDs for AVs. Additionally, intersection and mid-block crosswalks, as well as bicycle lanes, showed high risk scores due to frequent interactions with pedestrians and cyclists. These areas are particularly risky because they involve unpredictable movements from non-vehicular road users, which require AVs to make rapid adjustments in speed and trajectory. These findings provide a foundation for improving AV algorithms to enhance safety and establishing objective criteria for AV policy-making. Full article

(This article belongs to the Special Issue Sustainable Intelligent Transportation: Cooperative Systems and Vehicle Automation)

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20 pages, 3598 KiB

Open AccessArticle

Dynamic Multi-Function Lane Management for Connected and Automated Vehicles Considering Bus Priority

by Zhen Zhang, Lingfei Rong, Zhiquan Xie and Xiaoguang Yang

Sustainability 2024, 16(18), 8078; https://doi.org/10.3390/su16188078 - 15 Sep 2024

Viewed by 1650

Abstract

Bus lanes are commonly implemented to ensure absolute priority for buses at signalized intersections. However, while prioritizing buses, existing bus lane management strategies often exacerbate traffic demand imbalances among lanes. To address this issue, this paper proposes a dynamic Multi-Function Lane (MFL) management strategy. The proposed strategy transforms traditional bus lanes into Multi-Function Lanes (MFLs) that permit access to Connected and Automated Vehicles (CAVs). By fully utilizing the idle right-of-way of the MFL, the proposed strategy can achieve traffic efficiency improvement. To evaluate the proposed strategy, some experiments are conducted under various demand levels and CAV penetration rates. The results reveal that the proposed strategy (i) improves the traffic intensity balance degree by up to 52.9 under high demand levels; (ii) reduces delay by up to 80.56% and stops by up to 89.35% with the increase in demand level and CAV penetration rate; (iii) guarantees absolute bus priority under various demand levels and CAV penetration rates. The proposed strategy performs well even when CAV penetration is low. This indicates that the proposed strategy has the potential for real-world application. Full article

(This article belongs to the Special Issue Sustainable Intelligent Transportation: Cooperative Systems and Vehicle Automation)

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28 pages, 3620 KiB

Open AccessArticle

Smart Insertion Strategies for Sustainable Operation of Shared Autonomous Vehicles

by Sapan Tiwari, Neema Nassir and Patricia Sauri Lavieri

Sustainability 2024, 16(12), 5175; https://doi.org/10.3390/su16125175 - 18 Jun 2024

Cited by 3 | Viewed by 1641

Abstract

As shared autonomous vehicles (SAV) emerge as an economical and feasible mode of transportation in modern cities, effective optimization models are essential to simulate their service. Traditional optimization approaches, based on first-come-first-served principles, often result in sub-optimal outcomes and, more notably, can impact public transport (PT) operations by creating unnecessary competition. This study introduces four insertion strategies within the MATSim model of the Melbourne Metropolitan Area, addressing these challenges. Two strategies optimize SAV operations by considering overall network costs, and the other two make insertion decisions based on the available PT service in the network. The findings show that strategic insertions of the requests can significantly enhance SAV service quality by improving the vehicle load and decreasing vehicle and empty kilometers traveled per ride. The analysis indicates that these strategies are particularly effective for smaller fleet sizes, leading to an increased number of served rides and a more equitable distribution of wait times across the network, reflected in an improved Gini Index. The findings suggest that prioritization-based insertions significantly enhance service quality by prioritizing users with limited access to PT, ensuring that those with fewer PT options are served first, and encouraging a more integrated and sustainable urban transportation system. Full article

(This article belongs to the Special Issue Sustainable Intelligent Transportation: Cooperative Systems and Vehicle Automation)

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