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Advances in Intelligent Transportation, Smart Grids and Electric Vehicles in the Context of Sustainability

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

Deadline for manuscript submissions: 31 January 2026 | Viewed by 2531

Special Issue Editors

Dr. Yitong Shang

E-Mail Website
Guest Editor
Department of Civil and Environmental Engineering, The Hong Kong University of Science and Technology, Hong Kong 999077, China
Interests: electric vehicle; smart city; smart grids
Special Issues, Collections and Topics in MDPI journals
Dr. Xueqian Fu

E-Mail Website
Guest Editor
College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China
Interests: rural microgrid; agricultural energy internet; statistical machine learning; smart energy systems
Special Issues, Collections and Topics in MDPI journals
Dr. Qiang Xing

E-Mail
Guest Editor
School of Automation and Artificial Intelligence, Nanjing University of Posts and Telecommunications, Nanjing 210042, China
Interests: vehicle-to-grid; smart grid; transportation electrification; deep reinforcement learning; graph reinforcement learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent years, with the increasing emphasis on sustainability, the transportation industry has undergone profound transformations. Intelligent transportation systems and public transportation have emerged as crucial elements in the pursuit of sustainable mobility. They not only provide solutions to alleviate traffic congestion, reduce carbon emissions, and improve the overall travel experience but also establish closer connections with other critical infrastructures, such as the power grid.

Intelligent transportation technologies, such as connected vehicles, advanced traffic management systems, and intelligent parking solutions, are revolutionizing our travel patterns. These innovations optimize traffic flow, enhance safety, and improve the utilization efficiency of transportation infrastructure. Meanwhile, public transportation, including buses, metros, and light rails, remains the cornerstone of sustainable urban mobility, providing convenient and environmentally friendly options for commuters. In addition, the synergy between transportation and the power grid represents an emerging and equally significant aspect.

Given the complex technical and social challenges in this field, this special issue aims to gather cutting-edge research and insights. We welcome original research articles and reviews that explore the advancements in intelligent transportation and public transportation within the context of sustainability. Potential research areas include (but are not limited to):

  1. Modeling and optimization of intelligent transportation systems for energy-efficient traffic flow;
  2. Integration of emerging technologies (e.g., 5G, IoT) into public transportation to enhance service quality;
  3. Sustainable design and planning of public transportation networks, considering factors such as population density and travel demand;
  4. Development of intelligent energy management strategies for electric public transportation vehicles;
  5. The role of behavioral science in promoting the adoption of sustainable intelligent transportation and public transportation options;
  6. Modeling and optimization of intelligent transportation systems for energy-efficient traffic flow, considering the impact on the power grid;
  7. Development of intelligent energy management strategies for electric public transportation vehicles, including smart charging and V2G capabilities;
  8. Evaluation and improvement of the environmental and social impacts of intelligent transportation and public transportation initiatives.

Dr. Yitong Shang
Dr. Xueqian Fu
Dr. Qiang Xing
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 transportation
  • intelligent transportation systems
  • public transportation
  • energy-efficient traffic flow
  • vehicle-to-grid (V2G)
  • smart charging
  • emerging technologies
  • environmental and social impacts

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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

19 pages, 4853 KiB  
Article
Evaluating the Impact of AV Penetration and Behavior on Freeway Traffic Efficiency and Safety Using Microscopic Simulation
by Taebum Eom and Minju Park
Sustainability 2025, 17(12), 5536; https://doi.org/10.3390/su17125536 - 16 Jun 2025
Viewed by 519
Abstract
As autonomous vehicles (AVs) are gradually integrated into existing traffic systems, understanding their impact on freeway operations becomes essential for effective infrastructure planning and policy design. This study explores how AV penetration rates, behavior profiles, and freeway geometry interact to influence traffic performance [...] Read more.
As autonomous vehicles (AVs) are gradually integrated into existing traffic systems, understanding their impact on freeway operations becomes essential for effective infrastructure planning and policy design. This study explores how AV penetration rates, behavior profiles, and freeway geometry interact to influence traffic performance and safety. Using microscopic simulations in VISSIM (a high-fidelity traffic simulation tool), four typical freeway segment types—basic sections, weaving zones, on-ramp merging areas, and AV-exclusive lanes—were modeled under diverse traffic demands and AV behavior settings. The findings indicate that, while AVs can improve flow stability in simple environments, their performance may deteriorate in complex merging scenarios without supportive design or behavior coordination. AV-exclusive lanes offer some mitigation when AV share is high. These results underscore that AV integration requires context-specific strategies and cannot be universally applied. Adaptive, behavior-aware traffic management is recommended to support a smooth transition toward mixed autonomy. Full article
(This article belongs to the Special Issue Advances in Intelligent Transportation, Smart Grids and Electric Vehicles in the Context of Sustainability)
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24 pages, 1978 KiB  
Article
Decision Making for Energy Acquisition of Electric Vehicle Taxi with Profit Maximization
by Li Cui, Yanping Wang, Hongquan Qu, Yiqiang Li, Mingshen Wang and Qingyuan Wang
Sustainability 2025, 17(11), 5116; https://doi.org/10.3390/su17115116 - 3 Jun 2025
Viewed by 422
Abstract
With the emergence of joint business operations involving electric vehicle taxis (EVTs) and charging/swapping stations (CSSTs), a unified decision-making method has become essential for an EVT to select both the driving path and the energy acquisition mode (EAM). The decision making is influenced [...] Read more.
With the emergence of joint business operations involving electric vehicle taxis (EVTs) and charging/swapping stations (CSSTs), a unified decision-making method has become essential for an EVT to select both the driving path and the energy acquisition mode (EAM). The decision making is influenced by energy acquisition cost and potential operation profit. The energy acquisition cost is closely related to the driving time required to reach a CSST, and existing prediction methods for driving time ignore the spatial–temporal interactions of traffic flows on different roads and fail to account for traffic congestion differences across various sections of a road. Existing estimation methods for potential operation income ignore the distributions of taxi orders in different areas. To address these issues, a traffic flow prediction model is first proposed based on the long short-term memory–generative adversarial network (LSTM-GAN) deep learning algorithm. A refined driving time model is developed by segmenting a road into different sections. Then, an expected operation income model is developed considering the distributions of origins and destinations of taxi orders in different areas. Then, a decision-making method for path planning and the charging/swapping mode is proposed, aiming to maximize the total profit of EVTs. Finally, the effectiveness of the proposed decision-making method for EVTs is validated with a city’s traffic network. Full article
(This article belongs to the Special Issue Advances in Intelligent Transportation, Smart Grids and Electric Vehicles in the Context of Sustainability)
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21 pages, 663 KiB  
Article
Sustainable and Profitable Urban Transport: Implementing a ‘Tire-as-a-Service’ Model with Regrooving and Retreading
by Jérémie Schutz and Christophe Sauvey
Sustainability 2025, 17(9), 3892; https://doi.org/10.3390/su17093892 - 25 Apr 2025
Viewed by 547
Abstract
Rapid urbanization has intensified pressure on transport infrastructures, with urban bus networks playing a crucial role in promoting sustainable mobility. However, managing operational costs while minimizing environmental impacts remains a major challenge. This study investigates the innovative “Tire-as-a-Service” (TaaS) model applied to bus [...] Read more.
Rapid urbanization has intensified pressure on transport infrastructures, with urban bus networks playing a crucial role in promoting sustainable mobility. However, managing operational costs while minimizing environmental impacts remains a major challenge. This study investigates the innovative “Tire-as-a-Service” (TaaS) model applied to bus fleets, incorporating regrooving and retreading techniques to improve tire durability and efficiency. The TaaS model shifts the focus from purchasing tires to a service-based approach, where users pay according to usage (i.e., kilometers driven), promoting proactive maintenance and waste reduction. Solving this problem is based on a discrete-event simulation algorithm to optimize tire inspection schedules and, consequently, minimize total costs while guaranteeing a minimum level of service and reducing environmental impact. A robustness analysis will validate the model developed, thus contributing to a more sustainable urban transport system. Full article
(This article belongs to the Special Issue Advances in Intelligent Transportation, Smart Grids and Electric Vehicles in the Context of Sustainability)
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27 pages, 3130 KiB  
Article
Towards Sustainable Cities: A KPI-Based Method to Compare Cities’ Performance and Encourage the Spread of Electric Cars
by Alvaro Menendez Agudin, Claudia Caballini, Francesco Paolo Deflorio, Gregorio Fernandez Aznar, Leopold Herman and Klemen Knez
Sustainability 2025, 17(7), 3052; https://doi.org/10.3390/su17073052 - 29 Mar 2025
Viewed by 766
Abstract
European cities have adopted different solutions to address the challenges of charging infrastructure for electric vehicles, depending on their specific characteristics and needs. The widespread adoption of effective solutions could accelerate the transition towards more sustainable urban mobility. However, as cities differ in [...] Read more.
European cities have adopted different solutions to address the challenges of charging infrastructure for electric vehicles, depending on their specific characteristics and needs. The widespread adoption of effective solutions could accelerate the transition towards more sustainable urban mobility. However, as cities differ in socio-economic, infrastructural, and environmental aspects, a one-size-fits-all approach may not be suitable. Currently, there is a lack of studies in the literature that identify similarities among cities to support the development of shared strategies for sustainable electric mobility. This paper contributes to filling this gap by proposing a methodology based on Key Performance Indicators (KPIs) to classify and compare cities according to their electric vehicle infrastructure. Using quantitative data from 80 European cities across civil, social, and transport-related factors, as well as electric vehicle charging characteristics, we identified five reference city clusters. A sensitivity analysis, conducted across 30 scenarios, validated the robustness of the KPI framework. This approach provides a tool for policymakers to monitor the evolution of charging infrastructure, supporting data-driven decision-making for sustainable urban mobility. By promoting efficient and adaptable electric vehicle policies, this study aligns with the objectives of the 2030 Agenda for Sustainable Development, particularly in fostering sustainable cities and clean energy adoption. Full article
(This article belongs to the Special Issue Advances in Intelligent Transportation, Smart Grids and Electric Vehicles in the Context of Sustainability)
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