Measuring and Modelling the Concentration of Vehicle-Related PM2.5 and PM10 Emissions Based on Neural Networks

Shepelev, Vladimir; Glushkov, Aleksandr; Slobodin, Ivan; Cherkassov, Yuri

doi:10.3390/math11051144

Cited by 11 publications

(3 citation statements)

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“…Then, we should analyze the impact on these characteristics of vehicles belonging to other groups: group 3 (TV-3) (trucks weighing from 3.5 to 12 tons); 4 (TV-4) (trucks weighing over 12 tons) and 5 (TV-5) (buses weighing over 3.5 tons). Notably, this study does not consider group 2 (TV-2) vehicles (vans and minibuses weighing up to 3.5 tons), since previous study [43] have shown that they do not significantly change the temporal characteristics of the flow of vehicles belonging to the first group.…”

Section: Analysis Of the Vehicle Queue Structurementioning

confidence: 99%

Studying the Relationship between the Traffic Flow Structure, the Traffic Capacity of Intersections, and Vehicle-Related Emissions

Shepelev,

Glushkov,

Slobodin

et al. 2023

Mathematics

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This paper proposes a new approach to assessing the impact of changes in the traffic flow on pollutant emissions and the traffic capacity of signal-controlled intersections. We present an intelligent vision system tailored to monitor the traffic behavior at signal-controlled intersections in urban areas. Traffic cameras are used to collect real-time vehicle traffic data. Our system provides valuable insight into the relationship between traffic flows, emissions, and intersection capacity. This study shows how changes in the traffic composition reduce the traffic capacity of intersections and increase emissions, especially those involving fine dust particles. Using the combination of fuzzy logic methods and Gaussian spline distribution functions, we demonstrate the variability of these relationships and highlight the need to further study compromises between mobility and air quality. Ultimately, our results offer promising opportunities for the development of intelligent traffic management systems aimed at balancing the demands of urban mobility while minimizing environmental impact. This study demonstrates the importance of taking into account the correlation between the change in the composition of traffic queues due to a random change in the traffic flow and its impact on emissions and the traffic capacity of intersections. This study found that the presence of various groups of vehicles and their position in the queue can reduce the traffic capacity by up to 70% and increase the growth of harmful emissions by 14 fold.

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Section: Analysis Of the Vehicle Queue Structurementioning

confidence: 99%

Studying the Relationship between the Traffic Flow Structure, the Traffic Capacity of Intersections, and Vehicle-Related Emissions

Shepelev,

Glushkov,

Slobodin

et al. 2023

Mathematics

Self Cite

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“…Hybrid recommender systems integrate both contentbased and collaborative approaches, synergistically optimizing performance and honing the precision of recommendations. The amalgamation of these techniques empowers recommender systems to furnish users with suggestions that are not only personalized but also highly accurate [5][6][7][8].…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Movies Recommendation System Based on Demographics and Facial Expression Analysis using Machine Learning

Balfaqih

2023

IJACSA

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Cinemas and digital platforms offer an extensive array of content requiring tailored filtering to cater to individual preferences. While recommender systems prove invaluable for this purpose, conventional movie recommendations tend to emphasize specific attributes, leading to a reduction in overall accuracy and reliability. Notably, the extraction process of facial temporal attributes exhibits a suboptimal level of accuracy, thereby influencing the classification of attributes and the overall accuracy of the recommendation system. This article introduces a hybrid recommender system that seamlessly integrates collaborative filtering and content-based methodologies. The system takes into account crucial factors such as age, gender, emotion, and genre attributes. Films undergo an initial categorization based on genre, with a subsequent selection of the most representative genres to ascertain group preferences. Ratings for these selected movies are then predicted and organized in descending order. Employing Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) models, the system achieves real-time extraction of facial attributes, particularly enhancing the accuracy of emotion attribute extraction through sequential processing. The CNN model demonstrates a commendable 55.3% accuracy score, the LSTM model excels with a 59.1% score, while the combined CNN and LSTM models showcase an impressive 60.2% accuracy.The performance of the recommendation system is rigorously evaluated using standard metrics, including precision, recall, and F1-measure. Results underscore the superior performance of the proposed system across various testing scenarios compared to the established benchmark. Nevertheless, it is noteworthy that the precision of the benchmark marginally surpasses the proposed system in the age groups of 8-14 and 15-24.

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“…Air pollution, specifically the presence of particulate matter with a diameter of 10 µm or less (PM10), poses a significant threat to public health and the environment [1]. PM10 particles are small enough to be inhaled deep into the lungs, leading to a range of health problems [2].…”

Section: Introductionmentioning

confidence: 99%

Predicting PM10 Concentrations Using Evolutionary Deep Neural Network and Satellite-Derived Aerosol Optical Depth

Ghajari,

Kaveh,

Martín

2023

Mathematics

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Predicting particulate matter with a diameter of 10 μm (PM10) is crucial due to its impact on human health and the environment. Today, aerosol optical depth (AOD) offers high resolution and wide coverage, making it a viable way to estimate PM concentrations. Recent years have also witnessed in-creasing promise in refining air quality predictions via deep neural network (DNN) models, out-performing other techniques. However, learning the weights and biases of the DNN is a task classified as an NP-hard problem. Current approaches such as gradient-based methods exhibit significant limitations, such as the risk of becoming ensnared in local minimal within multi-objective loss functions, substantial computational requirements, and the requirement for continuous objective functions. To tackle these challenges, this paper introduces a novel approach that combines the binary gray wolf optimizer (BGWO) with DNN to improve the optimization of models for air pollution prediction. The BGWO algorithm, inspired by the behavior of gray wolves, is used to optimize both the weight and bias of the DNN. In the proposed BGWO, a novel sigmoid function is proposed as a transfer function to adjust the position of the wolves. This study gathers meteorological data, topographic information, PM10 pollution data, and satellite images. Data preparation includes tasks such as noise removal and handling missing data. The proposed approach is evaluated through cross-validation using metrics such as correlation rate, R square, root-mean-square error (RMSE), and accuracy. The effectiveness of the BGWO-DNN framework is compared to seven other machine learning (ML) models. The experimental evaluation of the BGWO-DNN method using air pollution data shows its superior performance compared with traditional ML techniques. The BGWO-DNN, CapSA-DNN, and BBO-DNN models achieved the lowest RMSE values of 16.28, 19.26, and 20.74, respectively. Conversely, the SVM-Linear and GBM algorithms displayed the highest levels of error, yielding RMSE values of 36.82 and 32.50, respectively. The BGWO-DNN algorithm secured the highest R2 (88.21%) and accuracy (93.17%) values, signifying its superior performance compared with other models. Additionally, the correlation between predicted and actual values shows that the proposed model surpasses the performance of other ML techniques. This paper also observes relatively stable pollution levels during spring and summer, contrasting with significant fluctuations during autumn and winter.

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Measuring and Modelling the Concentration of Vehicle-Related PM2.5 and PM10 Emissions Based on Neural Networks

Cited by 11 publications

References 55 publications

Studying the Relationship between the Traffic Flow Structure, the Traffic Capacity of Intersections, and Vehicle-Related Emissions

Studying the Relationship between the Traffic Flow Structure, the Traffic Capacity of Intersections, and Vehicle-Related Emissions

A Hybrid Movies Recommendation System Based on Demographics and Facial Expression Analysis using Machine Learning

Predicting PM10 Concentrations Using Evolutionary Deep Neural Network and Satellite-Derived Aerosol Optical Depth

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