Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
5.5
2.5
Journals
Applied Sciences
Special Issues
Advances in Intelligent Transportation and Its Applications
applsci-logo
Submit to Special Issue Submit Abstract to Special Issue Review for Applied Sciences Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

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: 20 July 2026 | Viewed by 4234

Special Issue Editors

Dr. Jaeho Lee

E-Mail Website
Guest Editor
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
Prof. Dr. Jinwoo Kim

E-Mail Website
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 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. 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

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (4 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

24 pages, 4417 KB  
Article
Collaborative Real-Time Single-Object Anomaly Detection Framework of Roadside Facilities for Traffic Safety and Management Using Efficient YOLO
by Jiheon Kang, Soohyen Jang, Yoonyoung Choi, Wooyong Lee and Byoungkug Kim
Appl. Sci. 2025, 15(20), 11139; https://doi.org/10.3390/app152011139 - 17 Oct 2025
Viewed by 671
Abstract
This paper proposes an Edge AI-based collaborative framework for real-time anomaly detection of roadside facilities to enhance traffic safety and management. Traditional detection methods rely on fixed cameras or manual inspections, which are time-consuming and inefficient. Our approach embeds lightweight YOLO models in [...] Read more.
This paper proposes an Edge AI-based collaborative framework for real-time anomaly detection of roadside facilities to enhance traffic safety and management. Traditional detection methods rely on fixed cameras or manual inspections, which are time-consuming and inefficient. Our approach embeds lightweight YOLO models in vehicle dashboard cameras to collect and analyze diverse video data across multiple vehicles in real time. This distributed system overcomes the limitations of individual vehicles through collaborative data aggregation and enables robust anomaly detection in various types of roadside facilities. We evaluate several YOLO variants to identify the optimal balance between detection accuracy and computational efficiency. Experimental results demonstrate improved anomaly detection precision and faster response times, validating the feasibility of our system for practical deployment. The proposed method offers a scalable and efficient solution for proactive traffic management and accident prevention by leveraging distributed edge intelligence. Full article
(This article belongs to the Special Issue Advances in Intelligent Transportation and Its Applications)
►▼ Show Figures

Figure 1

27 pages, 4446 KB  
Article
HAPS-PPO: A Multi-Agent Reinforcement Learning Architecture for Coordinated Regional Control of Traffic Signals in Heterogeneous Road Networks
by Qiong Lu, Haoda Fang, Zhangcheng Yin and Guliang Zhu
Appl. Sci. 2025, 15(20), 10945; https://doi.org/10.3390/app152010945 - 12 Oct 2025
Viewed by 1429
Abstract
The increasing complexity of urban traffic networks has highlighted the potential of Multi-Agent Reinforcement Learning (MARL) for Traffic Signal Control (TSC). However, most existing MARL methods assume homogeneous observation and action spaces among agents, ignoring the inherent heterogeneity of real-world intersections in topology [...] Read more.
The increasing complexity of urban traffic networks has highlighted the potential of Multi-Agent Reinforcement Learning (MARL) for Traffic Signal Control (TSC). However, most existing MARL methods assume homogeneous observation and action spaces among agents, ignoring the inherent heterogeneity of real-world intersections in topology and signal phasing, which limits their practical applicability. To address this gap, we propose HAPS-PPO (Heterogeneity-Aware Policy Sharing Proximal Policy Optimization), a novel MARL framework for coordinated signal control in heterogeneous road networks. HAPS-PPO integrates two key mechanisms: an Observation Padding Wrapper (OPW) that standardizes varying observation dimensions, and a Dynamic Multi-Strategy Grouping Learning (DMSGL) mechanism that trains dedicated policy heads for agent groups with distinct action spaces, enabling adequate knowledge sharing while maintaining structural correctness. Comprehensive experiments in a high-fidelity simulation environment based on a real-world road network demonstrate that HAPS-PPO significantly outperforms Fixed-time control and mainstream MARL baselines (e.g., MADQN, FMA2C), reducing average delay time by up to 44.74% and average waiting time by 59.60%. This work provides a scalable and plug-and-play solution for deploying MARL in realistic, heterogeneous traffic networks. Full article
(This article belongs to the Special Issue Advances in Intelligent Transportation and Its Applications)
►▼ Show Figures

Figure 1

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
Viewed by 657
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)
►▼ Show Figures

Figure 1

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
Cited by 2 | Viewed by 1075
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)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-4
Back to TopTop