Traffic Pollution Assessment Using Artificial Neural Network and Multivariate Analysis

Traffic Pollution Assessment Using Artificial Neural Network and Multivariate Analysis

The work addressed a study on pollution caused by traffic on the highway. In particular, it was considered the concentration of pollutant, resulting from the passage of vehicles on the freeway. Five different stations (sensors and samples) used to collect data. The data collection period around six...

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Bibliographic Details
Main Authors: Mario De Luca, Daiva Žilionienė, Saulius Gadeikis, Gianluca Dell’Acqua
Format: Article
Language:English
Published: RTU Press 2017-03-01
Series:The Baltic Journal of Road and Bridge Engineering
Subjects:
artificial neural network
concentration of pollutant
multivariate analysis
traffic flow rate
wind speed and direction
temperature.
Online Access:https://bjrbe-journals.rtu.lv/article/view/3312
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spelling doaj-868e33e035f64403b31e853a0a1aa1b42020-11-25T03:01:51ZengRTU PressThe Baltic Journal of Road and Bridge Engineering1822-427X1822-42882017-03-0112110.3846/bjrbe.2017.071808Traffic Pollution Assessment Using Artificial Neural Network and Multivariate AnalysisMario De Luca0Daiva Žilionienė1Saulius Gadeikis2Gianluca Dell’Acqua3Dept of Civil, Construction and Environmental Engineering, University of Naples Federico II, Via Claudio 21, I–80125 Naples, ItalyDept of Roads, Vilnius Gediminas Technical University, Saulėtekio al. 11, LT–10223 Vilnius, LithuaniaDept of Hydrogeology and Engineering Geology, Vilnius University, M.K.Čiurlionio g. 21/27, LT–03101 Vilnius, LithuaniaDept of Civil, Construction and Environmental Engineering, University of Naples Federico II, Via Claudio 21, I–80125 Naples, ItalyThe work addressed a study on pollution caused by traffic on the highway. In particular, it was considered the concentration of pollutant, resulting from the passage of vehicles on the freeway. Five different stations (sensors and samples) used to collect data. The data collection period around six months. Also, the following parameters were detected: wind speed and direction, temperature and traffic flow rate. Data processed with Multivariate Analysis and Artificial Neural Network approach. The best model it obtained with Artificial Neural Network approach. In fact, this model presented the best fit to the experimental data.https://bjrbe-journals.rtu.lv/article/view/3312artificial neural networkconcentration of pollutantmultivariate analysistraffic flow ratewind speed and directiontemperature.
collection DOAJ
language English
format Article
sources DOAJ
author Mario De Luca
Daiva Žilionienė
Saulius Gadeikis
Gianluca Dell’Acqua
spellingShingle Mario De Luca
Daiva Žilionienė
Saulius Gadeikis
Gianluca Dell’Acqua
Traffic Pollution Assessment Using Artificial Neural Network and Multivariate Analysis
The Baltic Journal of Road and Bridge Engineering
artificial neural network
concentration of pollutant
multivariate analysis
traffic flow rate
wind speed and direction
temperature.
author_facet Mario De Luca
Daiva Žilionienė
Saulius Gadeikis
Gianluca Dell’Acqua
author_sort Mario De Luca
title Traffic Pollution Assessment Using Artificial Neural Network and Multivariate Analysis
title_short Traffic Pollution Assessment Using Artificial Neural Network and Multivariate Analysis
title_full Traffic Pollution Assessment Using Artificial Neural Network and Multivariate Analysis
title_fullStr Traffic Pollution Assessment Using Artificial Neural Network and Multivariate Analysis
title_full_unstemmed Traffic Pollution Assessment Using Artificial Neural Network and Multivariate Analysis
title_sort traffic pollution assessment using artificial neural network and multivariate analysis
publisher RTU Press
series The Baltic Journal of Road and Bridge Engineering
issn 1822-427X
1822-4288
publishDate 2017-03-01
description The work addressed a study on pollution caused by traffic on the highway. In particular, it was considered the concentration of pollutant, resulting from the passage of vehicles on the freeway. Five different stations (sensors and samples) used to collect data. The data collection period around six months. Also, the following parameters were detected: wind speed and direction, temperature and traffic flow rate. Data processed with Multivariate Analysis and Artificial Neural Network approach. The best model it obtained with Artificial Neural Network approach. In fact, this model presented the best fit to the experimental data.
topic artificial neural network
concentration of pollutant
multivariate analysis
traffic flow rate
wind speed and direction
temperature.
url https://bjrbe-journals.rtu.lv/article/view/3312
work_keys_str_mv AT mariodeluca trafficpollutionassessmentusingartificialneuralnetworkandmultivariateanalysis
AT daivazilioniene trafficpollutionassessmentusingartificialneuralnetworkandmultivariateanalysis
AT sauliusgadeikis trafficpollutionassessmentusingartificialneuralnetworkandmultivariateanalysis
AT gianlucadellacqua trafficpollutionassessmentusingartificialneuralnetworkandmultivariateanalysis
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