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Advances in Intelligent Transportation and Its Applications

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Transportation and Future Mobility".

Deadline for manuscript submissions: 30 November 2025 | Viewed by 861

Special Issue Editors

Department of Software, Duksung Women's University, Seoul, Republic of Korea
Interests: smart grid networks; intelligent resource allocation scheme; meta-learning with self-improving momentum target

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Guest Editor
School of Semiconductor & Electronic Engineering, Yeungjin University, Daegu, Republic of Korea
Interests: wireless sensor network; Zigbee; Internet of Things; smart grid; power transmission network

Special Issue Information

Dear Colleagues,

The rapid evolution of Intelligent Transportation Systems (ITSs) is transforming urban mobility through the integration of AIoT technologies, smart computing, and intelligent sensor devices. These systems enhance traffic management, improve safety, and reduce our environmental impact by enabling the performance of real-time data analysis and automated decision-making. In addition, the emergence of Urban Air Mobility (UAM) equipped with advanced avionics systems further extends the scope of smart transportation. UAM leverages AI-based navigation, communication, and control technologies to enable safe and efficient low-altitude air travel. The convergence of these innovations paves the way for smarter, more connected, and sustainable urban mobility solutions.

Dr. Jaeho Lee
Prof. Dr. Jinwoo Kim
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. Applied Sciences 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

  • automotive
  • mobility
  • ITS
  • transportation
  • safety
  • AI-based intelligence
  • security and networks
  • sensing
  • object detection for transportation
  • visualization

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Published Papers (2 papers)

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Research

24 pages, 2536 KB  
Article
Lightweight Online Clock Skew Estimation for Robust ITS Time Synchronization
by Wooyong Lee
Appl. Sci. 2025, 15(19), 10581; https://doi.org/10.3390/app151910581 - 30 Sep 2025
Abstract
Precise time synchronization is indispensable for enabling seamless coordination in Intelligent Transportation Systems (ITS) which rely on reliable vehicle communications. This work introduces lightweight online clock skew compensation algorithms based on Recursive Least Squares (RLS) and Recursive Weighted Least Squares (RWLS) techniques tailored [...] Read more.
Precise time synchronization is indispensable for enabling seamless coordination in Intelligent Transportation Systems (ITS) which rely on reliable vehicle communications. This work introduces lightweight online clock skew compensation algorithms based on Recursive Least Squares (RLS) and Recursive Weighted Least Squares (RWLS) techniques tailored for ITS time synchronization. Unlike traditional approaches relying on offline batch processing and large-scale data storage, the proposed algorithms continuously update clock skew estimates immediately upon receiving each timing sample, thereby significantly reducing memory requirements. These methods are applicable to Vehicle-to-Vehicle (V2V), Vehicle-to-Infrastructure (V2I), and Infrastructure-to-Infrastructure (I2I) communication scenarios, offering a cost-effective software solution to improve synchronization accuracy. Extensive simulations and experimental validations demonstrate that the developed estimators effectively minimize skew-related timing errors, thereby enhancing the robustness and precision of vehicular network timekeeping. Full article
(This article belongs to the Special Issue Advances in Intelligent Transportation and Its Applications)
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16 pages, 2587 KB  
Article
Video Display Improvement by Using Collaborative Edge Devices with YOLOv11
by Byoungkug Kim, Soohyun Wang and Jaeho Lee
Appl. Sci. 2025, 15(17), 9241; https://doi.org/10.3390/app15179241 - 22 Aug 2025
Viewed by 525
Abstract
Efficient human detection in video streams is essential for various IoT applications, including surveillance, smart cities, intelligent transportation systems (ITSs), and industrial automation. However, resource-constrained IoT devices often face limitations in handling deep learning-based object detection. This study proposes a collaborative edge computing [...] Read more.
Efficient human detection in video streams is essential for various IoT applications, including surveillance, smart cities, intelligent transportation systems (ITSs), and industrial automation. However, resource-constrained IoT devices often face limitations in handling deep learning-based object detection. This study proposes a collaborative edge computing framework utilizing multiple Raspberry Pi-based IoT devices to improve YOLOv11-based human detection performance. By distributing video frames across multiple edge devices, the proposed system effectively balances the computational load, resulting in an increase in the FPS (Frames Per Second) for processed video outputs. The experimental results confirm that as more edge devices collaborate, overall video processing efficiency improves, demonstrating the feasibility of distributed object detection for scalable and cost-effective IoT-based video analytics. In particular, the proposed approach holds significant potential for ITS applications such as pedestrian monitoring at intersections, real-time incident detection, and enhancing traffic safety by enabling responsive and decentralized analysis at the edge. Full article
(This article belongs to the Special Issue Advances in Intelligent Transportation and Its Applications)
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