Cover Image

Modeling of CO emissions from traffic vehicles using artificial neural networks

Traffic emissions are considered one of the leading causes of environmental impact in megacities and their dangerous effects on human health. This paper presents a hybrid model based on data mining and GIS models designed to predict vehicular Carbon Monoxide (CO) emitted from traffic on the New Klan...

Full description

Saved in:
Bibliographic Details
Main Authors: Al-Gbur, Omer Saud Azeez, Pradhan, Biswajeet, Mohd Shafri, Helmi Zulhaidi, Shukla, Nagesh, Lee, Chang Wook, Rizeei, Hossein Mojaddadi
Format: Article
Language:English
Published: Multidisciplinary Digital Publishing Institute (MDPI) 2019
Online Access:http://psasir.upm.edu.my/id/eprint/81380/1/Modeling%20of%20CO%20emissions%20from%20traffic%20vehicles%20using%20artificial%20neural%20networks.pdf
http://psasir.upm.edu.my/id/eprint/81380/
https://www.mdpi.com/2076-3417/9/2/313
Tags: Add Tag
No Tags, Be the first to tag this record!
id my.upm.eprints.81380
record_format eprints
spelling my.upm.eprints.813802024-01-23T03:22:37Z http://psasir.upm.edu.my/id/eprint/81380/ Modeling of CO emissions from traffic vehicles using artificial neural networks Al-Gbur, Omer Saud Azeez Pradhan, Biswajeet Mohd Shafri, Helmi Zulhaidi Shukla, Nagesh Lee, Chang Wook Rizeei, Hossein Mojaddadi Traffic emissions are considered one of the leading causes of environmental impact in megacities and their dangerous effects on human health. This paper presents a hybrid model based on data mining and GIS models designed to predict vehicular Carbon Monoxide (CO) emitted from traffic on the New Klang Valley Expressway, Malaysia. The hybrid model was developed based on the integration of GIS and the optimized Artificial Neural Network algorithm that combined with the Correlation based Feature Selection (CFS) algorithm to predict the daily vehicular CO emissions and generate prediction maps at a microscale level in a small urban area by using a field survey and open source data, which are the main contributions to this paper. The other contribution is related to the case study, which represents the spatial and quantitative variations in the vehicular CO emissions between toll plaza areas and road networks. The proposed hybrid model consists of three steps: the first step is the implementation of the correlation-based Feature Selection model to select the best model’s predictors; the second step is the prediction of vehicular CO by using a multilayer perceptron neural network model; and the third step is the creation of micro scale prediction maps. The model was developed using six traffic CO predictors: number of vehicles, number of heavy vehicles, number of motorbikes, temperature, wind speed and a digital surface model. The network architecture and its hyperparameters were optimized through a grid search approach. The traffic CO concentrations were observed at 15-min intervals on weekends and weekdays, four times per day. The results showed that the developed model had achieved validation accuracy of 80.6 %. Overall, the developed models are found to be promising tools for vehicular CO simulations in highly congested areas. Multidisciplinary Digital Publishing Institute (MDPI) 2019 Article PeerReviewed text en http://psasir.upm.edu.my/id/eprint/81380/1/Modeling%20of%20CO%20emissions%20from%20traffic%20vehicles%20using%20artificial%20neural%20networks.pdf Al-Gbur, Omer Saud Azeez and Pradhan, Biswajeet and Mohd Shafri, Helmi Zulhaidi and Shukla, Nagesh and Lee, Chang Wook and Rizeei, Hossein Mojaddadi (2019) Modeling of CO emissions from traffic vehicles using artificial neural networks. Applied Sciences, 9. art. no. 313. pp. 1-23. ISSN 2076-3417 https://www.mdpi.com/2076-3417/9/2/313 10.3390/app9020313
institution Universiti Putra Malaysia
building UPM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Putra Malaysia
content_source UPM Institutional Repository
url_provider http://psasir.upm.edu.my/
language English
description Traffic emissions are considered one of the leading causes of environmental impact in megacities and their dangerous effects on human health. This paper presents a hybrid model based on data mining and GIS models designed to predict vehicular Carbon Monoxide (CO) emitted from traffic on the New Klang Valley Expressway, Malaysia. The hybrid model was developed based on the integration of GIS and the optimized Artificial Neural Network algorithm that combined with the Correlation based Feature Selection (CFS) algorithm to predict the daily vehicular CO emissions and generate prediction maps at a microscale level in a small urban area by using a field survey and open source data, which are the main contributions to this paper. The other contribution is related to the case study, which represents the spatial and quantitative variations in the vehicular CO emissions between toll plaza areas and road networks. The proposed hybrid model consists of three steps: the first step is the implementation of the correlation-based Feature Selection model to select the best model’s predictors; the second step is the prediction of vehicular CO by using a multilayer perceptron neural network model; and the third step is the creation of micro scale prediction maps. The model was developed using six traffic CO predictors: number of vehicles, number of heavy vehicles, number of motorbikes, temperature, wind speed and a digital surface model. The network architecture and its hyperparameters were optimized through a grid search approach. The traffic CO concentrations were observed at 15-min intervals on weekends and weekdays, four times per day. The results showed that the developed model had achieved validation accuracy of 80.6 %. Overall, the developed models are found to be promising tools for vehicular CO simulations in highly congested areas.
format Article
author Al-Gbur, Omer Saud Azeez
Pradhan, Biswajeet
Mohd Shafri, Helmi Zulhaidi
Shukla, Nagesh
Lee, Chang Wook
Rizeei, Hossein Mojaddadi
spellingShingle Al-Gbur, Omer Saud Azeez
Pradhan, Biswajeet
Mohd Shafri, Helmi Zulhaidi
Shukla, Nagesh
Lee, Chang Wook
Rizeei, Hossein Mojaddadi
Modeling of CO emissions from traffic vehicles using artificial neural networks
author_facet Al-Gbur, Omer Saud Azeez
Pradhan, Biswajeet
Mohd Shafri, Helmi Zulhaidi
Shukla, Nagesh
Lee, Chang Wook
Rizeei, Hossein Mojaddadi
author_sort Al-Gbur, Omer Saud Azeez
title Modeling of CO emissions from traffic vehicles using artificial neural networks
title_short Modeling of CO emissions from traffic vehicles using artificial neural networks
title_full Modeling of CO emissions from traffic vehicles using artificial neural networks
title_fullStr Modeling of CO emissions from traffic vehicles using artificial neural networks
title_full_unstemmed Modeling of CO emissions from traffic vehicles using artificial neural networks
title_sort modeling of co emissions from traffic vehicles using artificial neural networks
publisher Multidisciplinary Digital Publishing Institute (MDPI)
publishDate 2019
url http://psasir.upm.edu.my/id/eprint/81380/1/Modeling%20of%20CO%20emissions%20from%20traffic%20vehicles%20using%20artificial%20neural%20networks.pdf
http://psasir.upm.edu.my/id/eprint/81380/
https://www.mdpi.com/2076-3417/9/2/313
_version_ 1789426936723800064
score 13.211869

Similar Items