Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
6.1
2.6
Journals
Electronics
Special Issues
AI Innovations in Smart Transportation
share announcement

AI Innovations in Smart Transportation

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Artificial Intelligence".

Deadline for manuscript submissions: 15 October 2026 | Viewed by 1166

Special Issue Editors

Dr. Chengjia Han

E-Mail Website
Guest Editor
School of Civil and Environment Engineering, Nanyang Technological University, Singapore 639798, Singapore
Interests: intelligent maintenance management of asphalt pavement; structural health monitoring; pavement distress detection based on computer vision; time series forecasting; building information modeling
Special Issues, Collections and Topics in MDPI journals
Dr. Zheng Tong

E-Mail Website
Guest Editor
School of Transportation, Southeast University, Nanjing 211189, China
Interests: prompt-guided large language models; evidential deep neural networks; decision-making under uncertainty; AI-enabled ground-penetrating radar detection
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Intelligent Transportation Systems (ITSs) represent a transformative approach to urban mobility, seamlessly integrating people, vehicles, and road networks through advanced information and communication technologies. By leveraging artificial intelligence (AI), ITSs enhance traffic efficiency, reduce congestion, and improve road safety, making these systems indispensable for addressing the growing challenges of modern transportation.

Recent advancements in AI have significantly impacted two critical domains within this field: smart transportation infrastructure (e.g., roads, bridges, tunnels) and autonomous vehicle technology. This Special Issue explores cutting-edge AI applications in these areas, including the following:

  1. AI-driven infrastructure damage detection to facilitate early maintenance;
  2. AI-based structural health monitoring to ensure longevity;
  3. Digital twins for real-time infrastructure simulation and management;
  4. AI-assisted transportation design to optimize urban planning;
  5. Smart sensor systems enabling the reliable implementation of autonomous driving;
  6. AI-powered route planning for dynamic traffic adaptation;
  7. Autonomous vehicle control systems for safe navigation;
  8. Eco-friendly smart transport solutions to reduce emissions and energy consumption.

These innovations highlight AI’s pivotal role in shaping the future of sustainable, efficient, and resilient transportation networks. Researchers and practitioners are invited to contribute insights on these emerging trends to drive further progress in this field.

Dr. Chengjia Han
Dr. Zheng Tong
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. 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

  • intelligent transportation system
  • artificial intelligence
  • transport infrastructure
  • digital twin
  • autonomous driving
  • pattern recognition
  • sustainability

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 (2 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

18 pages, 11012 KB  
Article
Lightweight Multi-Task UAV Detection for V2X Security Using HA-EffNet
by Zhu Xu and Yanzan Sun
Electronics 2026, 15(8), 1654; https://doi.org/10.3390/electronics15081654 - 15 Apr 2026
Viewed by 304
Abstract
Unauthorized unmanned aerial vehicles (UAVs) threaten Vehicle-to-Everything (V2X) spectrum security. Real-time edge detection faces strict hardware constraints, severe multipath fading, and Doppler distortions. This article proposes HA-EffNet, a physics-informed multi-task learning framework engineered for radio frequency (RF) sensing on roadside units (RSUs). The [...] Read more.
Unauthorized unmanned aerial vehicles (UAVs) threaten Vehicle-to-Everything (V2X) spectrum security. Real-time edge detection faces strict hardware constraints, severe multipath fading, and Doppler distortions. This article proposes HA-EffNet, a physics-informed multi-task learning framework engineered for radio frequency (RF) sensing on roadside units (RSUs). The network restricts its temporal receptive field to align mathematically with the channel coherence time, thereby preventing deep noise overfitting. A hierarchical mechanism integrates Efficient Channel Attention (ECA) for shallow noise suppression and Receptive Field Attention (RFA) for deep signature extraction. Furthermore, the shared multi-task architecture simultaneously executes discrete classification and continuous spectral parameter regression, effectively halving computational overhead compared to redundant single-task deployments. Evaluations on the Microphase and DroneRFa datasets yield classification accuracies of 97.88% and 94.67%. Compound tests integrating Tapped Delay Line C (TDL-C) models and dynamic signal-to-noise ratio (SNR) variations validate algorithmic resilience against severe physical degradation. Utilizing a 0.12-million-parameter footprint, the network delivers a 0.84 ms inference latency and 1204.9 frames per second (FPS) throughput on the NVIDIA Jetson Orin Nano Super, providing a highly efficient edge-sensing solution. Full article
(This article belongs to the Special Issue AI Innovations in Smart Transportation)
►▼ Show Figures

Figure 1

16 pages, 729 KB  
Article
Mamba-Based Macro–MicroSpatio-Temporal Model for Traffic Flow Prediction
by Haoning Lv, Fayang Lan and Weijie Xiu
Electronics 2026, 15(6), 1327; https://doi.org/10.3390/electronics15061327 - 23 Mar 2026
Viewed by 355
Abstract
Traffic flow prediction plays an important role in intelligent transportation systems. However, accurately modeling traffic dynamics remains challenging due to complex temporal correlations and spatial interactions across road networks. In this work, we propose a Mamba-based macro–micro spatio-temporal model for traffic flow prediction. [...] Read more.
Traffic flow prediction plays an important role in intelligent transportation systems. However, accurately modeling traffic dynamics remains challenging due to complex temporal correlations and spatial interactions across road networks. In this work, we propose a Mamba-based macro–micro spatio-temporal model for traffic flow prediction. Unlike graph-based approaches that rely on predefined adjacency matrices to model spatial relationships, our method treats sensor nodes as sequence elements and applies Mamba blocks along the spatial dimension. Through the global receptive field of the structured state space model, spatial dependencies are implicitly learned without requiring explicit graph structures. The proposed architecture consists of stacked spatio-temporal blocks, each composed of two Macro Feature Blocks and one Micro Feature Block. The Macro Feature Blocks are designed to capture global temporal dependencies and spatial interactions across all nodes, while the Micro Feature Block focuses on modeling localized spatio-temporal patterns at a finer granularity. By applying structured state space modeling along both temporal and spatial dimensions, the model is able to capture long-range temporal dependencies and global spatial correlations without relying on explicit graph structures. Experiments conducted on four real-world datasets demonstrate that the proposed model achieves competitive or improved performance compared with existing baseline methods under standard evaluation metrics. Full article
(This article belongs to the Special Issue AI Innovations in Smart Transportation)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-2
Back to TopTop