Advanced Technologies and Artificial Intelligence for Sustainable and Intelligent Transportation Systems: Second Edition

A special issue of Inventions (ISSN 2411-5134). This special issue belongs to the section "Inventions and Innovation in Electrical Engineering/Energy/Communications".

Deadline for manuscript submissions: 30 April 2026 | Viewed by 1270

Special Issue Editors


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Guest Editor
Department of Electronic Engineering and Computer Science, School of Science and Technology, Hong Kong Metropolitan University, Hong Kong, China
Interests: big data; bioinformatics; computational intelligence; data science; energy monitoring and management; intelligent transportation; optimization
Special Issues, Collections and Topics in MDPI journals

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Special Issue Information

Dear Colleagues,

This Special Issue is a continuation of a previous successful issue, “Advanced Technologies and Artificial Intelligence for Sustainable and Intelligent Transportation Systems”. Intelligent Transportation Systems (ITS) have become essential components of modern mobility infrastructure. Beyond the fundamental requirements of safety, affordability, and accessibility, there is increasing emphasis being placed on sustainability as a guiding mission.

Over the years, substantial progress has been made in research on and the development of ITS. However, with the growing complexity of transportation networks, there is a pressing need to enhance and upgrade existing systems using cutting-edge technologies and artificial intelligence (AI). This aligns with global goals outlined in initiatives such as the United Nations Sustainable Development Goals and the European Commission’s Mobility and Transport policies.

This Special Issue aims to showcase high-quality, original research on recent advances in AI and emerging technologies in the transportation domain. We particularly encourage contributions that highlight state-of-the-art theories, novel methodologies, and innovative systems for the design, development, deployment, and application of these technologies. Submissions that offer theoretical insights, technological innovations, and real-world or pilot case studies are equally welcome.

Topics of interest include, but are not limited to, the following:

  • Large-scale data collection, storage, and processing for ITS
  • Deep learning applications in ITS
  • Transfer learning for transportation systems
  • Big data analytics and technologies for ITS
  • Autonomous and semi-autonomous vehicles
  • Edge, fog, and cloud computing for transportation infrastructure
  • Cybersecurity challenges and solutions in ITS
  • Video, image, and signal processing techniques for transportation systems
  • We look forward to your valuable contributions that will help shape the future of sustainable and intelligent transportation systems.

Dr. Kwok Tai Chui
Prof. Dr. Brij B. Gupta
Guest Editors

Manuscript Submission Information

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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
  • big data
  • computational intelligence
  • deep learning
  • intelligent transportation systems
  • Internet of Things

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Published Papers (1 paper)

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Research

33 pages, 4268 KB  
Article
AI-Driven Digital Twin for Optimizing Solar Submersible Pumping Systems
by Yousef Salah, Omar Shalash, Esraa Khatab, Mostafa Hamad and Sherif Imam
Inventions 2025, 10(6), 93; https://doi.org/10.3390/inventions10060093 - 25 Oct 2025
Cited by 2 | Viewed by 920
Abstract
Reliable water access in remote and desert-like regions remains a challenge, particularly in areas with limited infrastructure. Solar-powered submersible pumps offer a promising solution; however, optimizing their performance under variable environmental conditions remains a challenging task. This research presents an Artificial Intelligence (AI)-driven [...] Read more.
Reliable water access in remote and desert-like regions remains a challenge, particularly in areas with limited infrastructure. Solar-powered submersible pumps offer a promising solution; however, optimizing their performance under variable environmental conditions remains a challenging task. This research presents an Artificial Intelligence (AI)-driven digital twin framework for modeling and optimizing the performance of a solar-powered submersible pump system. The proposed system has three core components: (1) an AI model for predicting the inverter motor’s output frequency based on the current generated by the solar panels, (2) a predictive model for estimating the pump’s generated power based on the inverter motor’s output, and (3) a mathematical formulation for determining the volume of water lifted based on the system’s operational parameters. Moreover, a dataset comprising 6 months of environmental and system performance data was collected and utilized to train and evaluate multiple predictive models. Unlike previous works, this research integrates real-world data with a multi-phase AI modeling pipeline for real-time water output estimation. Performance assessments indicate that the Random Forest (RF) model outperformed alternative approaches, achieving the lowest error rates with a Mean Absolute Error (MAE) of 1.00 Hz for output frequency prediction and 1.39 kW for pump output power prediction. The framework successfully estimated annual water delivery of 166,132.77 m3, with peak monthly output of 18,276.96 m3 in July and minimum of 9784.20 m3 in January demonstrating practical applicability for agricultural water management planning in arid regions. Full article
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