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Sensors in Intelligent Transport Systems
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Sensors in Intelligent Transport Systems

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".

Deadline for manuscript submissions: 30 October 2025 | Viewed by 3576

Special Issue Editors

Dr. Alexander Kocian

E-Mail Website
Guest Editor
Department of Computer Science, University of Pisa, 56127 Pisa, Italy
Interests: Internet of Things; machine learning; dynamic processes; agro-informatics; health informatics
Special Issues, Collections and Topics in MDPI journals
Dr. Ana Lucia Martins

E-Mail Website
Guest Editor
Iscte Business School, Iscte—University Institute of Lisbon, 1649-026 Lisboa, Portugal
Interests: operations management; supply chain management; technologies in the support of supply chain management
Dr. Mirco Nanni

E-Mail Website
Guest Editor
Institute of Information Science and Technologies, National Research Council of Italy (ISTI-CNR), 56124 Pisa, Italy
Interests: mobility analytics; temporal and spatio-temporal data mining; sustainable mobility; mobility simulation
Dr. Luca Pappalardo

E-Mail Website
Guest Editor
1. Institute of Information Science and Technologies, National Research Council of Italy (ISTI-CNR), 56124 Pisa, Italy
2. Scuola Normale Superiore, 56126 Pisa, Italy
Interests: mobility data science; computational social science; human-centered AI; human–AI coevolution
Special Issues, Collections and Topics in MDPI journals
Dr. Paolo Milazzo

E-Mail Website
Guest Editor
Department of Computer Science, University of Pisa, 56127 Pisa, Italy
Interests: modeling and simulation of complex systems; formal verification; biomedical systems analysis; bioinformatics

Special Issue Information

Dear Colleagues,

As society develops, the relevance of Intelligent Transport Systems (ITS) becomes increasingly visible. ITS can make transport safer, more efficient, and more sustainable by applying information and communication technologies to all transportation modes. Moreover, the integration of existing technologies can create new services. The full potential of ITS will be more visible if deployed worldwide. To achieve this goal, research poses as a major player in developing and deploying key ITS technologies and contributing to standardization, interoperability between transport modes and countries, and cross-border continuity of services, among other uses.

This Special Issue aims to collect high-quality original articles or comprehensive review papers on ITS technologies. Particular interest is devoted to EAI INTSYS 2024. Authors of conference papers that fall within the scope of Sensors are invited to submit extended papers to this Special Issue. The topics for this Special Issue include, but are not limited to, the following:

  • Sensors, detectors and actuators in ITS;
  • AI and deep learning in ITS;
  • Autonomous driving, connected cars;
  • Intelligent vehicle routing;
  • Human–AI interaction in ITS;
  • Computer vision and environment perception in ITS;
  • Intelligent vehicles;
  • V2X communications in ITS;
  • Vehicle localization.

Dr. Alexander Kocian
Dr. Ana Lucia Martins
Dr. Mirco Nanni
Dr. Luca Pappalardo
Dr. Paolo Milazzo
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. Sensors 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 2600 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 transport systems
  • mobility data science
  • geospatial AI

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

Published Papers (3 papers)

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23 pages, 1080 KiB  
Article
Interoperable Traceability in Agrifood Supply Chains: Enhancing Transport Systems Through IoT Sensor Data, Blockchain, and DataSpace
by Giovanni Farina, Alexander Kocian, Gianluca Brunori, Stefano Chessa, Maria Bonaria Lai, Daniele Nardi, Claudio Schifanella, Susanna Bonura, Nicola Masi, Sergio Comella, Fiorenzo Ambrosino, Angelo Mariano, Lucio Colizzi, Giovanna Maria Dimitri, Marco Gori, Franco Scarselli, Silvia Bonomi, Enrico Almici, Luca Antiga, Antonio Salvatore Fiorentino and Lucio Moreschiadd Show full author list remove Hide full author list
Sensors 2025, 25(11), 3419; https://doi.org/10.3390/s25113419 - 29 May 2025
Viewed by 319
Abstract
Traceability plays a critical role in ensuring the quality, safety, and transparency of supply chains, where transportation stakeholders are fundamental to the efficient movement of goods. However, the diversity of actors involved poses significant challenges to achieving these goals. Each organization typically operates [...] Read more.
Traceability plays a critical role in ensuring the quality, safety, and transparency of supply chains, where transportation stakeholders are fundamental to the efficient movement of goods. However, the diversity of actors involved poses significant challenges to achieving these goals. Each organization typically operates its own information system, tailored to manage internal data, but often lacks the ability to communicate effectively with external systems. Moreover, when data exchange between different systems is required, it becomes critical to maintain full control over the shared data and to manage access rights precisely. In this work, we propose the concept of interoperable traceability. We present a model that enables the seamless integration of data from sensors, IoT devices, data management platforms, and distributed ledger technologies (DLT) within a newly designed data space architecture. We also demonstrate a practical implementation of this concept by applying it to real-world scenarios in the agri-food sector, with direct implications for transportation systems and all stakeholders in a supply chain. Our demonstrator supports the secure exchange of traceability data between existing systems, providing stakeholders with a novel approach to managing and auditing data with increased transparency and efficiency. Full article
(This article belongs to the Special Issue Sensors in Intelligent Transport Systems)
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24 pages, 5618 KiB  
Article
Federated Learning-Based Predictive Traffic Management Using a Contained Privacy-Preserving Scheme for Autonomous Vehicles
by Tariq Alqubaysi, Abdullah Faiz Al Asmari, Fayez Alanazi, Ahmed Almutairi and Ammar Armghan
Sensors 2025, 25(4), 1116; https://doi.org/10.3390/s25041116 - 12 Feb 2025
Cited by 1 | Viewed by 1297
Abstract
Intelligent Transport Systems (ITSs) are essential for secure and privacy-preserving communications in Autonomous Vehicles (AVs) and enhance facilities like connectivity and roadside assistance. Earlier research models used for traffic management compromised user privacy and exposed sensitive data to potential adversaries while handling real-time [...] Read more.
Intelligent Transport Systems (ITSs) are essential for secure and privacy-preserving communications in Autonomous Vehicles (AVs) and enhance facilities like connectivity and roadside assistance. Earlier research models used for traffic management compromised user privacy and exposed sensitive data to potential adversaries while handling real-time data from numerous vehicles. This research introduces a Federated Learning-based Predictive Traffic Management (FLPTM) system designed to optimize service access and privacy for Autonomous Vehicles (AVs) within an ITS. Moreover, a CPPS will provide strong security to mitigate adversarial threats through state modelling and authenticated access permissions for the integrity of vehicle communication networks from man-in-the-middle attacks. The suggested FLPTM system utilizes a Contained Privacy-Preserving Scheme (CPPS) that decentralizes data processing and allows vehicles to train local models without sharing raw data. The CPPS framework combines a classifier-based learning technique with state modelling and access permissions to protect user data against invasions and man-in-the-middle attacks. The proposed model leverages Federated Learning (FL) to enhance data security in collaborative machine learning processes by allowing updates that preserve privacy, enabling joint learning without exposing raw data. It addresses key challenges such as high communication costs, the impact of adversarial attacks, and access time inefficiencies. Using FL, the model reduces communication costs by 23.29%, mitigates adversarial effects by 16.1%, and improves access time by 18.95%, achieving significant cost savings and maintaining data privacy throughout the learning process. Full article
(This article belongs to the Special Issue Sensors in Intelligent Transport Systems)
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16 pages, 2351 KiB  
Article
A Weather-Adaptive Convolutional Neural Network Framework for Better License Plate Detection
by Utsha Saha, Binita Saha and Md Ashique Imran
Sensors 2024, 24(23), 7841; https://doi.org/10.3390/s24237841 - 8 Dec 2024
Cited by 1 | Viewed by 1302
Abstract
Automatic License Plate Recognition (ALPR) systems are essential for Intelligent Transport Systems (ITS), effective transportation management, security, law enforcement, etc. However, the performance of ALPR systems can be significantly affected by environmental conditions such as heavy rain, fog, and pollution. This paper introduces [...] Read more.
Automatic License Plate Recognition (ALPR) systems are essential for Intelligent Transport Systems (ITS), effective transportation management, security, law enforcement, etc. However, the performance of ALPR systems can be significantly affected by environmental conditions such as heavy rain, fog, and pollution. This paper introduces a weather-adaptive Convolutional Neural Network (CNN) framework that leverages the YOLOv10 model that is designed to enhance license plate detection in adverse weather conditions. By incorporating weather-specific data augmentation techniques, our framework improves the robustness of ALPR systems under diverse environmental scenarios. We evaluate the effectiveness of this approach using metrics such as precision, recall, F1, mAP50, and mAP50-95 score across various model configurations and augmentation strategies. The results demonstrate a significant improvement in overall detection performance, particularly in challenging weather conditions. This study provides a promising solution for deploying resilient ALPR systems in regions with similar environmental complexities. Full article
(This article belongs to the Special Issue Sensors in Intelligent Transport Systems)
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