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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: closed (30 October 2022) | Viewed by 33774

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/recognizing gestures in sign languages; human emotions and its applications in affective computing and social robotics
Special Issues, Collections and Topics 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
Special Issues, Collections and Topics in MDPI journals
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
Special Issues, Collections and Topics in MDPI journals
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, UK
Interests: interpretable and explainable AI; self-explainable and intelligible AI; interpretable and explainable data science and analytics
Special Issues, Collections and Topics in MDPI journals
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, Collections and Topics 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 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.

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Published Papers (4 papers)

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Research

24 pages, 1627 KiB  
Article
A Novel Markov Model-Based Traffic Density Estimation Technique for Intelligent Transportation System
by Hira Beenish, Tariq Javid, Muhammad Fahad, Adnan Ahmed Siddiqui, Ghufran Ahmed and Hassan Jamil Syed
Sensors 2023, 23(2), 768; https://doi.org/10.3390/s23020768 - 9 Jan 2023
Cited by 9 | Viewed by 3167
Abstract
An intelligent transportation system (ITS) aims to improve traffic efficiency by integrating innovative sensing, control, and communications technologies. The industrial Internet of things (IIoT) and Industrial Revolution 4.0 recently merged to design the industrial Internet of things–intelligent transportation system (IIoT-ITS). IIoT sensing technologies [...] Read more.
An intelligent transportation system (ITS) aims to improve traffic efficiency by integrating innovative sensing, control, and communications technologies. The industrial Internet of things (IIoT) and Industrial Revolution 4.0 recently merged to design the industrial Internet of things–intelligent transportation system (IIoT-ITS). IIoT sensing technologies play a significant role in acquiring raw data. The application continuously performs the complex task of managing traffic flows effectively based on several parameters, including the number of vehicles in the system, their location, and time. Traffic density estimation (TDE) is another important derived parameter desirable to keep track of the dynamic state of traffic volume. The expanding number of vehicles based on wireless connectivity provides new potential to predict traffic density more accurately and in real time as previously used methodologies. We explore the topic of assessing traffic density by using only a few simple metrics, such as the number of surrounding vehicles and disseminating beacons to roadside units and vice versa. This research paper investigates TDE techniques and presents a novel Markov model-based TDE technique for ITS. Finally, an OMNET++-based approach with an implementation of a significant modification of a traffic model combined with mathematical modeling of the Markov model is presented. It is intended for the study of real-world traffic traces, the identification of model parameters, and the development of simulated traffic. Full article
(This article belongs to the Special Issue Machine Learning Methods for Intelligent Transportation Infrastructure (ITI) Systems for Urban Environments)
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17 pages, 4503 KiB  
Article
An End-to-End Automated License Plate Recognition System Using YOLO Based Vehicle and License Plate Detection with Vehicle Classification
by Reda Al-batat, Anastassia Angelopoulou, Smera Premkumar, Jude Hemanth and Epameinondas Kapetanios
Sensors 2022, 22(23), 9477; https://doi.org/10.3390/s22239477 - 4 Dec 2022
Cited by 30 | Viewed by 9906
Abstract
An accurate and robust Automatic License Plate Recognition (ALPR) method proves surprising versatility in an Intelligent Transportation and Surveillance (ITS) system. However, most of the existing approaches often use prior knowledge or fixed pre-and-post processing rules and are thus limited by poor generalization [...] Read more.
An accurate and robust Automatic License Plate Recognition (ALPR) method proves surprising versatility in an Intelligent Transportation and Surveillance (ITS) system. However, most of the existing approaches often use prior knowledge or fixed pre-and-post processing rules and are thus limited by poor generalization in complex real-life conditions. In this paper, we leverage a YOLO-based end-to-end generic ALPR pipeline for vehicle detection (VD), license plate (LP) detection and recognition without exploiting prior knowledge or additional steps in inference. We assess the whole ALPR pipeline, starting from vehicle detection to the LP recognition stage, including a vehicle classifier for emergency vehicles and heavy trucks. We used YOLO v2 in the initial stage of the pipeline and remaining stages are based on the state-of-the-art YOLO v4 detector with various data augmentation and generation techniques to obtain LP recognition accuracy on par with current proposed methods. To evaluate our approach, we used five public datasets from different regions, and we achieved an average recognition accuracy of 90.3% while maintaining an acceptable frames per second (FPS) on a low-end GPU. Full article
(This article belongs to the Special Issue Machine Learning Methods for Intelligent Transportation Infrastructure (ITI) Systems for Urban Environments)
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17 pages, 6799 KiB  
Article
Obstacle Avoidance of Multi-Sensor Intelligent Robot Based on Road Sign Detection
by Jianwei Zhao, Jianhua Fang, Shouzhong Wang, Kun Wang, Chengxiang Liu and Tao Han
Sensors 2021, 21(20), 6777; https://doi.org/10.3390/s21206777 - 12 Oct 2021
Cited by 10 | Viewed by 6656
Abstract
The existing ultrasonic obstacle avoidance robot only uses an ultrasonic sensor in the process of obstacle avoidance, which can only be avoided according to the fixed obstacle avoidance route. Obstacle avoidance cannot follow additional information. At the same time, existing robots rarely involve [...] Read more.
The existing ultrasonic obstacle avoidance robot only uses an ultrasonic sensor in the process of obstacle avoidance, which can only be avoided according to the fixed obstacle avoidance route. Obstacle avoidance cannot follow additional information. At the same time, existing robots rarely involve the obstacle avoidance strategy of avoiding pits. In this study, on the basis of ultrasonic sensor obstacle avoidance, visual information is added so the robot in the process of obstacle avoidance can refer to the direction indicated by road signs to avoid obstacles, at the same time, the study added an infrared ranging sensor, so the robot can avoid potholes. Aiming at this situation, this paper proposes an intelligent obstacle avoidance design of an autonomous mobile robot based on a multi-sensor in a multi-obstruction environment. A CascadeClassifier is used to train positive and negative samples for road signs with similar color and shape. A multi-sensor information fusion is used for path planning and the obstacle avoidance logic of the intelligent robot is designed to realize autonomous obstacle avoidance. The infrared sensor is used to obtain the environmental information of the ground depression on the wheel path, the ultrasonic sensor is used to obtain the distance information of the surrounding obstacles and road signs, and the information of the road signs obtained by the camera is processed by the computer and transmitted to the main controller. The environment information obtained is processed by the microprocessor and the control command is output to the execution unit. The feasibility of the design is verified by analyzing the distance acquired by the ultrasonic sensor, infrared distance measuring sensors, and the model obtained by training the sample of the road sign, as well as by experiments in the complex environment constructed manually. Full article
(This article belongs to the Special Issue Machine Learning Methods for Intelligent Transportation Infrastructure (ITI) Systems for Urban Environments)
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16 pages, 5047 KiB  
Article
Real-Time In-Vehicle Air Quality Monitoring System Using Machine Learning Prediction Algorithm
by Chew Cheik Goh, Latifah Munirah Kamarudin, Ammar Zakaria, Hiromitsu Nishizaki, Nuraminah Ramli, Xiaoyang Mao, Syed Muhammad Mamduh Syed Zakaria, Ericson Kanagaraj, Abdul Syafiq Abdull Sukor and Md. Fauzan Elham
Sensors 2021, 21(15), 4956; https://doi.org/10.3390/s21154956 - 21 Jul 2021
Cited by 37 | Viewed by 11852
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
(This article belongs to the Special Issue Machine Learning Methods for Intelligent Transportation Infrastructure (ITI) Systems for Urban Environments)
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