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Intelligent Transportation for Integrated Mobile System: AI-Driven Technologies, Engineering Systems,...
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Intelligent Transportation for Integrated Mobile System: AI-Driven Technologies, Engineering Systems, and Industrial Applications

A special issue of Technologies (ISSN 2227-7080).

Deadline for manuscript submissions: 30 September 2026 | Viewed by 2081

Special Issue Editors

Prof. Dr. Chih-Yu Hsu

E-Mail Website
Guest Editor
School of Cyber Science and Engineering, Wuxi University, Wuxi 214105, China
Interests: artificial intelligence; deep learning; machine learning
Special Issues, Collections and Topics in MDPI journals
Dr. Trong-The Nguyen

E-Mail Website
Guest Editor
Multimedia Communications Lab, University of Information Technology, VNU, Ho Chi Minh City, Vietnam
Interests: energy-efficient routing protocols; machine learning in WSNs; intrusion detection systems (IDS) for WSNs; WSNs in IoT (Internet of Things)
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Goutam Chakraborty

E-Mail Website
Guest Editor
Faculty of Software Information Science, Iwate Prefectural University, Takizawa 020-0693, Japan
Interests: pattern recognition; machine learning; data analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Transportation systems are rapidly evolving toward Transportation Intelligence, driven by advances in artificial intelligence, intelligent sensing, cyber–physical systems, and data-driven technologies. Beyond traditional intelligent transportation systems, transportation intelligence emphasizes system-level perception, learning, and adaptive decision-making across integrated and multi-modal mobility networks.

This Special Issue focuses on AI-driven methods, intelligent engineering systems, and applied technologies for transportation intelligence. It aims to bridge advanced computational techniques with transportation engineering practice, covering road transportation as the core, rail and urban transit as key extensions, and smart ports and logistics as integral components of integrated mobility systems.

Topics include, but are not limited to, the following: artificial intelligence and machine learning for transportation systems; transportation big data analytics and intelligent decision support; intelligent traffic planning, organization, and control; smart transportation infrastructure and digital twins; intelligent sensing, perception, and control technologies; vehicle–infrastructure cooperation (V2X); multi-modal transportation intelligence; smart ports, digital shipping, and intelligent logistics; and industry-oriented case studies and applications.

This Special Issue welcomes high-quality contributions that advance transportation intelligence technologies and support real-world deployment.

Prof. Dr. Chih-Yu Hsu
Dr. Trong-The Nguyen
Prof. Dr. Goutam Chakraborty
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. Technologies is an international peer-reviewed open access monthly 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 1800 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

  • artificial intelligence
  • machine learning
  • transportation systems
  • transportation big data analytics
  • intelligent traffic planning
  • smart transportation
  • intelligent sensing, perception, and control
  • technologies vehicle–infrastructure cooperation (V2X)
  • multi-modal transportation intelligence
  • smart ports
  • digital shipping
  • intelligent logistics

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Published Papers (2 papers)

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22 pages, 18777 KB  
Article
LSOD-YOLO: A Visual Object Detection Method for AGV Perception Systems Based on a Lightweight Backbone and Detection Head
by Sijing Cai, Zhanzheng Wu, Kang Liu, Tianbai Zhang, Wei Weng and Xiaoyi Zheng
Technologies 2026, 14(3), 173; https://doi.org/10.3390/technologies14030173 - 12 Mar 2026
Viewed by 1011
Abstract
In smart logistics and intelligent manufacturing scenarios, the deployment of Autonomous Guided Vehicles (AGVs) necessitates vision systems that balance stringent real-time constraints with high detection accuracy. However, contemporary lightweight models often struggle with multi-scale feature representation and precision degradation. To address these challenges, [...] Read more.
In smart logistics and intelligent manufacturing scenarios, the deployment of Autonomous Guided Vehicles (AGVs) necessitates vision systems that balance stringent real-time constraints with high detection accuracy. However, contemporary lightweight models often struggle with multi-scale feature representation and precision degradation. To address these challenges, this study presents LSOD-YOLO, a tailored evolution of YOLO11n designed for embedded AGV systems. Our methodology focuses on three architectural innovations: (1) we propose a Lightweight Shared Convolution Detection (LSCD) head integrated with Group Normalization (GN) and a scale-adaptive mechanism to harmonize multi-scale feature responses; (2) we re-engineer the backbone using a Star-Net architecture enhanced by Gated MLPs and Depthwise Attention to refine local spatial modeling; and (3) we integrate multi-branch residuals and Channel Attention (CAA) into the C3k2-Star-CAA module to enhance robustness against occlusions and complex backgrounds. The experimental validation on a self-built AGV industrial dataset and COCO128 reveals a compelling performance leap: a 30 FPS increase in throughput and a 1.5% gain in precision, all achieved with 32.8% fewer parameters. These findings confirm that LSOD-YOLO achieves a superior trade-off between computational efficiency and reliability, showing great potential for seamless deployment in resource-constrained AGV visual tasks. Full article
(This article belongs to the Special Issue Intelligent Transportation for Integrated Mobile System: AI-Driven Technologies, Engineering Systems, and Industrial Applications)
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Review

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26 pages, 2767 KB  
Review
Understanding Maritime Traffic Complexity: A Comprehensive Concept Development Review
by Vice Milin, Branko Lalić, Tatjana Stanivuk and Matko Maleš
Technologies 2026, 14(4), 231; https://doi.org/10.3390/technologies14040231 - 16 Apr 2026
Viewed by 667
Abstract
Maritime traffic complexity (MTC) is a term that has gained increased importance in the last decade in the maritime safety domain. It is a concept for understanding navigational safety and operational challenges in congested maritime environments. Although research interest in MTC has grown, [...] Read more.
Maritime traffic complexity (MTC) is a term that has gained increased importance in the last decade in the maritime safety domain. It is a concept for understanding navigational safety and operational challenges in congested maritime environments. Although research interest in MTC has grown, it is a concept that remains fragmented, with various interpretations of definitions, indicators, and modeling approaches present in the literature. This study presents a comprehensive literature review and bibliometric analysis to synthesize the current state of research on MTC as a scientific construct and clarify its conceptual foundations from an analytical perspective. In accordance with PRISMA guidelines and systematic literature review (SLR) methodology, relevant studies were identified and screened across major scientific databases. A detailed analysis was conducted on 40 scientific publications. The findings indicate that most existing MTC models rely mainly on Automatic Identification System (AIS) data and corresponding derived metrics. MTC is primarily assessed through geometric vessel–vessel interactions, relative motion parameters, and collision-risk indicators. Bibliometric analysis demonstrates a rapid increase in scientific interest in this topic since 2015, with research concentrated in several leading journals. The study identifies a significant methodological limitation in current frameworks, which often overlook the heterogeneity of marine traffic, environmental conditions, vessel reliability, and human factors. Therefore, this study highlights the need for a more comprehensive MTC evaluation framework that incorporates operational, geographical constraint-based, environmental, and behavioral variables alongside traditional AIS-based metrics. Full article
(This article belongs to the Special Issue Intelligent Transportation for Integrated Mobile System: AI-Driven Technologies, Engineering Systems, and Industrial Applications)
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