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Electronics
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The New Technologies and Applications on Intelligent Transportation Systems (ITS)
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The New Technologies and Applications on Intelligent Transportation Systems (ITS)

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Electrical and Autonomous Vehicles".

Deadline for manuscript submissions: 15 April 2026 | Viewed by 747

Special Issue Editors

Dr. Metin Mutlu Aydın

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Guest Editor
Department of Civil Engineering, Ondokuz Mayıs University, 55270 Samsun, Türkiye
Interests: intelligent transportation systems; sensors; deep learning; urban mobility; smart cities
Dr. Jonas Matijosius

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Guest Editor
Institute of Mechanical Science, Vilnius Gediminas Technical University, LT-10105 Vilnius, Lithuania
Interests: transportation; routing; intelligent transportation systems; transport ecology; mechanical systems
Special Issues, Collections and Topics in MDPI journals
Dr. Kristina Čižiūnienė

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Guest Editor
Department of Logistics and Transport Management, Transport Engineering Faculty, Vilnius Gediminas Technical University, Plytinės str. 25, LT-10105 Vilnius, Lithuania
Interests: logistics; optimization; transportation; transport engineering; supply chain; sustainability; human resources management; routing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue is focused on “The New Technologies and Applications on Intelligent Transportation Systems (ITS)”. In recent years, ITS have undergone transformative changes fueled by rapid developments in sensing and communication technologies, artificial intelligence, and big data analytics. These advancements have significantly enhanced transportation safety, efficiency, and sustainability. The integration of real-time data from heterogeneous sensors, combined with intelligent algorithms, enables dynamic traffic control, predictive maintenance, and improved user experiences for both public and private transportation. Moreover, the rise of autonomous vehicles and connected infrastructure demands robust, low-latency communication and reliable sensing solutions to ensure safe and efficient operation.

In recent years, intelligent transportation systems (ITS) have witnessed substantial growth due to advancements in AI, machine learning, Internet of Things (IoT), and big data analytics, significantly transforming urban mobility, infrastructure planning, and transport operations. The widespread adoption of connected and autonomous vehicles highlights the urgent need to address cybersecurity threats and ensure robust data privacy measures. Concurrently, the continuous rise of urban populations has stimulated research in real-time traffic management and predictive analytics, aiming to alleviate congestion, reduce emissions, and enhance overall transportation safety and efficiency. The integration of smart infrastructure with vehicle-to-everything (V2X) communication technologies has further emphasized the importance of developing interoperable and adaptive systems capable of managing diverse urban scenarios and user behaviors.

For this Special Issue, we invite original research and review papers describing state-of-the-art methods and innovative applications addressing the following topics:

  • Advanced sensing technologies for vehicle, infrastructure, and traffic monitoring and control;
  • V2X communication protocols and network optimization;
  • AI and machine learning for traffic prediction and management;
  • Autonomous and connected vehicle systems;
  • Smart infrastructure integration and interoperability solutions;
  • Edge computing and real-time data processing in ITS;
  • Cybersecurity and privacy-preserving methods in transportation networks;
  • Sustainable and energy-efficient ITS solutions;
  • Human factors and user-centric design in ITS.

Dr. Metin Mutlu Aydın
Dr. Jonas Matijosius
Dr. Kristina Čižiūnienė
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 systems (ITS)
  • new technologies in ITS
  • deep learning
  • artificial intelligence in transport
  • ITS solutions
  • smart traffic and infrastructure

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Published Papers (2 papers)

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Research

14 pages, 5168 KB  
Article
The Concept of a Digital Twin in the Arctic Environment
by Ari Pikkarainen, Timo Sukuvaara, Kari Mäenpää, Hannu Honkanen and Pyry Myllymäki
Electronics 2026, 15(5), 1001; https://doi.org/10.3390/electronics15051001 - 28 Feb 2026
Viewed by 174
Abstract
A Digital Twin is a virtual environment that simulates, predicts, and optimizes the performance of its physical counterpart. Digital Twin models hold great potential in wireless networking testing and development. This paper aims to envision our concept of simulating the operation of different [...] Read more.
A Digital Twin is a virtual environment that simulates, predicts, and optimizes the performance of its physical counterpart. Digital Twin models hold great potential in wireless networking testing and development. This paper aims to envision our concept of simulating the operation of different sensors in vehicle test-track conditions. Vehicle parameters are embedded into the edge computing entity, which uses them to generate a test configuration for the Digital Twin. This configuration is then applied in simulated sensor-output prediction, ultimately producing event data for the vehicle entity. The sensor suite—comprising radar, cameras, GPS and LiDAR—is modeled to provide the multi-modal input required for generating simulated perception data in the Digital Twin. To ensure realistic perception behavior, the physical vehicle is represented within a digital environment that reproduces the actual test track. This allows LiDAR occlusions to be attributed to genuine environmental structures (e.g., trees, buildings, other vehicles) rather than simulation artifacts. Within the Digital Twin, the objective is to evaluate how sensor signals—such as radar waves and LiDAR light pulses—propagate through the environment and how real-world obstacles may weaken or distort them. Historical datasets are used to calibrate and validate the Digital Twin, ensuring that the simulated sensor behavior aligns with real-world observations; the data collected during previous test runs can be used for visualization and analysis. Weather conditions are modeled to evaluate how rain, fog and snow impact sensor performance within the Digital Twin environment, to learn about the effects and predict sensor operation in different weather conditions. In this article, we examine the Digital Twin of our test track as a development environment for designing, deploying and testing ITS-enhanced road-weather services and warnings. These services integrate real-world road-weather observations, forecast data, roadside sensors and on-board vehicle measurements to support safe driving and optimize vehicle trajectories for both passenger and autonomous vehicles. This research is expected to benefit stakeholders involved in automotive testing, simulation and road-weather service development. Full article
(This article belongs to the Special Issue The New Technologies and Applications on Intelligent Transportation Systems (ITS))
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51 pages, 4232 KB  
Article
Intelligent Charging Reservation and Trip Planning of CAEVs and UAVs
by Palwasha W. Shaikh, Hussein T. Mouftah and Burak Kantarci
Electronics 2026, 15(2), 440; https://doi.org/10.3390/electronics15020440 - 19 Jan 2026
Viewed by 274
Abstract
Connected and Autonomous Electric Vehicles (CAEVs) and Uncrewed Aerial Vehicles (UAVs) are critical components of future Intelligent Transportation Systems (ITS), yet their deployment remains constrained by fragmented charging infrastructures and the lack of coordinated reservation and trip planning across static, dynamic wireless, and [...] Read more.
Connected and Autonomous Electric Vehicles (CAEVs) and Uncrewed Aerial Vehicles (UAVs) are critical components of future Intelligent Transportation Systems (ITS), yet their deployment remains constrained by fragmented charging infrastructures and the lack of coordinated reservation and trip planning across static, dynamic wireless, and vehicle-to-vehicle (V2V) charging networks using magnetic resonance and laser-based power transfer. Existing solutions often struggle with misalignment sensitivity, unpredictable arrivals, and disconnected ground–aerial scheduling. This work introduces a three-layer architecture that integrates a handshake protocol for coordinated charging and billing, a misalignment correction algorithm for magnetic resonance and laser-based systems, and three scheduling strategies: Static Heuristic Charging Scheduling and Planning (SH-CSP), Dynamic Heuristic Charging Scheduling and Planning (DH-CSP), and the Safety, Scheduling, and Sustainability-Aware Feasibility-Enhanced Deep Deterministic Policy Gradient (SAFE-DDPG). SAFE-DDPG extends vanilla DDPG with feasibility-aware action filtering, prioritized replay, and adaptive exploration to enable real-time scheduling in heterogeneous and congested charging networks. Results show that SAFE-DDPG significantly improves scheduling efficiency, reducing average wait times by over 70% compared to DH-CSP and over 85% compared to SH-CSP, demonstrating its potential to support scalable and coordinated ground–aerial charging ecosystems. Full article
(This article belongs to the Special Issue The New Technologies and Applications on Intelligent Transportation Systems (ITS))
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