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Special Issue "Machine Learning Methods for Intelligent Transportation Infrastructure (ITI) Systems for Urban Environments"

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

Deadline for manuscript submissions: 20 December 2021.

Special Issue Editors

Dr. Anastassia Angelopoulou
E-Mail Website
Guest Editor
Department of Computer Science, Faculty of Science and Technology at the University of Westminster, London W1B 2HW, UK
Interests: Computer Vision and Machine Learning with emphasis on tracking/recognising gestures in sign languages; human emotions and its applications in Affective Computing and Social Robotics
Special Issues and Collections in MDPI journals
Dr. Jude Hemanth
E-Mail Website
Guest Editor
Department of Electronics & Communication Engineering, Karunya University, Tamil Nadu 641114, India
Interests: Machine Learning; Computer Vision; Neural Networks and Artificial Intelligence; Pattern Recognition
Dr. Peter M. Roth
E-Mail Website
Guest Editor
Institute of Computer Graphics and Visio, Graz University of Technology, 8010 Graz, Austria
Interests: Visual Learning; Visual Surveillance; Object Detection; Object Tracking
Dr. Epameinondas Kapetanios
E-Mail Website
Guest Editor
School of Physics, Engineering, and Computer Science, Department of Computer Science, University of Hertfordshire, London W1W 6UW, United Kingdom
Interests: Interpretable and Explainable AI;Self-explainable and Intelligible AI; Interpretable and Explainable Data Science and Analytics
Prof. Dr. Jose Garcia Rodriguez
E-Mail Website
Guest Editor
Computer Technology Department, University of Alicante, 03080 Alicante, Spain
Interests: computer vision; machine learning; ambient intelligence; HPC
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

Traffic noise exposure, air pollution, road injuries, and traffic delays are some of the major problems with which residents are faced with on a daily basis in urban areas. Urban cities are facing serious environmental and quality-of-life problems due to a significant growth of vehicles, inadequate transport infrastructure, and lack of road-safety policies. For example, in many urban cities there is violation from heavy trucks to the normal roadways which leads to traffic congestion and delays. In addition, many cyclists experience frequent near misses due to the fact that cyclist’s clothing, posture changing, partial occlusions, and different observation angles all play a very challenging role in the recognition rates of the Machine Learning (ML) algorithms.

Over the last ten years, there has been an increasing interest in using machine learning and deep learning methods to analyze and visualize massive data generated from various sources in order to improve the classification and recognition of pedestrians, bicycles, special vehicles detection (e.g., emergency vehicles vs heavy trucks), and License Plate Recognition (LPR) for a safer and sustainable environment. Although deep models can capture a large variation of appearances, environment adaptation is required.

This Special Issue is designed to serve researchers and developers to publish original, innovative, and state-of-the-art machine learning methods, algorithms and architectures to analyze the modern vision of an intelligent transportation infrastructure system. Innovative solutions in the form of efficient visual object learning algorithms, prediction models and environmental sensors, which will take into account several important factors (e.g., quality of life, environment and traffic capabilities, etc.) are needed for sustainable Intelligent Transportation Systems. We are particularly interested in candidates who have conducted research in: a) ML based detection/classification: We are interested in systems, algorithms, methodologies that monitor road behavior (e.g., time-road usage violation, speed limit, special lanes overtaken, etc.) and filter different types of heavy trucks (e.g., emergency vehicles are permitted to break road rules), b) Environmental sensors and controllers: We are interested in traffic management models that gather data information from the streets via different sensors, such as cameras, microphones for noise assessments, low-cost sensors to measure air pollution, and provide recommendations to bypass city areas with abnormal noise and air pollution but with a sense of traveling times.

Dr. Peter M. Roth
Prof. Dr. Jose Garcia Rodriguez
Dr. Jude Hemanth
Dr. Anastassia Angelopoulou
Dr. Epameinondas Kapetanios
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 papers will be 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 2200 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.

Published Papers (1 paper)

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Research

Article
Real-Time In-Vehicle Air Quality Monitoring System Using Machine Learning Prediction Algorithm
Sensors 2021, 21(15), 4956; https://doi.org/10.3390/s21154956 - 21 Jul 2021
Viewed by 400
Abstract
This paper presents the development of a real-time cloud-based in-vehicle air quality monitoring system that enables the prediction of the current and future cabin air quality. The designed system provides predictive analytics using machine learning algorithms that can measure the drivers’ drowsiness and [...] Read more.
This paper presents the development of a real-time cloud-based in-vehicle air quality monitoring system that enables the prediction of the current and future cabin air quality. The designed system provides predictive analytics using machine learning algorithms that can measure the drivers’ drowsiness and fatigue based on the air quality presented in the cabin car. It consists of five sensors that measure the level of CO2, particulate matter, vehicle speed, temperature, and humidity. Data from these sensors were collected in real-time from the vehicle cabin and stored in the cloud database. A predictive model using multilayer perceptron, support vector regression, and linear regression was developed to analyze the data and predict the future condition of in-vehicle air quality. The performance of these models was evaluated using the Root Mean Square Error, Mean Squared Error, Mean Absolute Error, and coefficient of determination (R2). The results showed that the support vector regression achieved excellent performance with the highest linearity between the predicted and actual data with an R2 of 0.9981. Full article
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