Advanced Intelligent Control and Optimization for Autonomous and Electric Vehicles
A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Electrical and Autonomous Vehicles".
Deadline for manuscript submissions: 15 July 2026 | Viewed by 196
Special Issue Editors
2. MOE Key Laboratory for Urban Transportation Complex Systems Theory and Technology, Beijing Jiaotong University, Beijing 100044, China
3. School of Transportation and Civil Engineering, Nantong University, Nantong 226019, China
Interests: intelligent, connected, automated vehicle system control; integrated energy management and transportation planning; autonomous public transport vehicle development; pure electric vehicle scheduling; electric transportation planning and management
Special Issues, Collections and Topics in MDPI journals
Interests: comprehensive transportation planning; urban traffic control; rail transit operation management; traffic safety
Special Issues, Collections and Topics in MDPI journals
Interests: machine learning; large language models; interdisciplinary application in transportation; vision-language models
Interests: transportation planning and management; multi-modal transportation; train scheduling; railway dynamic-capacity allocation; demand-responsive transit services
Special Issue Information
Dear Colleagues,
In this Special Issue, we invite researchers to contribute their relevant, rigorous, and innovative work on autonomous transport development. We are particularly interested in AI technologies and their applications in facilitating autonomous transport optimization and network planning. Multiple transportation modes—rail, road, air, and maritime—are highly welcome. The focus is on demonstrating how recent technological advancements can support a net-zero economy, social justice, travel quality, and even a brand-new development direction, thereby shaping a better future for our smart transport network.
- As transportation develops fast, the emergence of AI-driven smart mobility signifies an unprecedented fusion of cutting-edge technology in transportation. A ‘prediction + optimization’ concept, i.e., the integration of AI-enhanced optimization, deep learning algorithms, large language models, tourism transportation, electric rail transit, electric vehicles, electric vessels, and electric drones, embodies a pioneering effort to promote automation. The era of smart mobility, harnessing the immense potential of management and optimization models, is poised to revolutionize the efficiency, automation, and user experience of transit systems.
- The shift towards AI-driven transportation is not merely an upgrade; it is a significant evolution. AI-infused next-generation ‘prediction-then-optimization’ transportation development is expected to offer not only enhanced reliability and energy efficiency but also unparalleled flexibility in urban mobility. Simultaneously, AI-supported scheduling optimization is set to reshape operations and management science, ensuring better scheduling and network planning.
- While the future holds great promise, the emerging technologies in scheduling optimization are still in their infancy. The challenges are numerous, but the opportunities for innovative research are equally abundant. This Special Issue aims to explore this expansive field, welcoming contributions that capture the innovative intersection of new technology and autonomous transportation.
In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:
- New mobility powered by AI: Design, operations, management, and service enhancements.
- Natural language processing for passenger experience: leveraging NLP to analyze passenger feedback, improve customer service interactions, and provide real-time travel information via conversational AI.
- Large language models for intelligent transport systems: using LLMs to analyze the large-scale data from transportation networks, providing insights for policy-making and operational efficiency.
- Multimodal transport: integrating multimodal data (e.g., vision, text, and audio) to improve the performance and safety of trains/vehicles/drones/ships by enhancing the decision-making capabilities.
- AI-driven tourism transportation: tourist travel route planning, monitoring of tourist safety, and demand-responsive tourism transit services.
- Scheduling optimization considering disruptions: timetabling adjustment, bus bridging, and connection to other modes of transportation.
- Smart urban rail transit: passenger demand forecasting, passenger controlling and inducing, train timetabling, rolling stock circulation planning.
- Safer transportation circumstances: dangerous goods’ routing.
We look forward to receiving your contributions.
Dr. Zhichao Cao
Prof. Dr. Zhenzhou Yuan
Dr. Tianbo Ji
Dr. Xiaojuan Li
Prof. Dr. Yonghao Yin
Guest Editors
Manuscript Submission Information
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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. Electronics 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
- autonomous smart transportation
- advanced intelligent control
- AI-driven mobility
- prediction-then-optimization
- intelligent transport systems
- deep learning applications
- smart urban rail transit
- scheduling and rescheduling optimization
- large language models (LLMs)
- electric and automated vehicles/vessels/drones
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