Development of Seasonal ARIMA Models for Traffic Noise Forecasting
In this paper, a time series analysis approach is adopted to monitor and predict a traffic noise levels dataset, measured in a site of Messina, Italy. In general, acoustical noise shows a high prediction complexity, since its slope is strongly related to the variability of the sources and to intrins...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
EDP Sciences
2017-01-01
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| Series: | MATEC Web of Conferences |
| Online Access: | https://doi.org/10.1051/matecconf/201712505013 |
| _version_ | 1818344052064518144 |
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| author | Guarnaccia Claudio Mastorakis Nikos E. Quartieri Joseph Tepedino Carmine Kaminaris Stavros D. |
| author_facet | Guarnaccia Claudio Mastorakis Nikos E. Quartieri Joseph Tepedino Carmine Kaminaris Stavros D. |
| author_sort | Guarnaccia Claudio |
| collection | DOAJ |
| description | In this paper, a time series analysis approach is adopted to monitor and predict a traffic noise levels dataset, measured in a site of Messina, Italy. In general, acoustical noise shows a high prediction complexity, since its slope is strongly related to the variability of the sources and to intrinsic randomness. In the analysed site the predominant source is road traffic, that has a periodic and non-stationary behaviour. The study of the time evolution of this hazardous agent is very useful to assess the impact to human health and activities. The time series models adopted in this paper are of the stochastic seasonal ARIMA class; these types of model are based on the strong periodicity registered in the acoustical equivalent levels. The observed periodicity is related to the highly variability of urban traffic in the different days of the week. Three different seasonal ARIMA models are proposed and calibrated on a rich dataset of 800 sound level measurements. The predictive capabilities of these techniques are encouraging. The implemented models show a good forecasting performances in terms of low residuals, i.e. difference between observed and estimated noise values. The residuals are analysed by means of statistical indexes, plots and tests. |
| first_indexed | 2024-12-13T16:40:20Z |
| format | Article |
| id | doaj.art-0584a29974ca4cc38d2dd63ee3d09577 |
| institution | Directory Open Access Journal |
| issn | 2261-236X |
| language | English |
| last_indexed | 2024-12-13T16:40:20Z |
| publishDate | 2017-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | MATEC Web of Conferences |
| spelling | doaj.art-0584a29974ca4cc38d2dd63ee3d095772022-12-21T23:38:18ZengEDP SciencesMATEC Web of Conferences2261-236X2017-01-011250501310.1051/matecconf/201712505013matecconf_cscc2017_05013Development of Seasonal ARIMA Models for Traffic Noise ForecastingGuarnaccia Claudio0Mastorakis Nikos E.1Quartieri Joseph2Tepedino Carmine3Kaminaris Stavros D.4Department of Civil Engineering, University of SalernoDepartment of Electrical Engineering and Computer Science, Hellenic Naval AcademyDepartment of Civil Engineering, University of SalernoDepartment of Civil Engineering, University of SalernoDepartment of Electrical Engineering, Piraeus University of Applied SciencesIn this paper, a time series analysis approach is adopted to monitor and predict a traffic noise levels dataset, measured in a site of Messina, Italy. In general, acoustical noise shows a high prediction complexity, since its slope is strongly related to the variability of the sources and to intrinsic randomness. In the analysed site the predominant source is road traffic, that has a periodic and non-stationary behaviour. The study of the time evolution of this hazardous agent is very useful to assess the impact to human health and activities. The time series models adopted in this paper are of the stochastic seasonal ARIMA class; these types of model are based on the strong periodicity registered in the acoustical equivalent levels. The observed periodicity is related to the highly variability of urban traffic in the different days of the week. Three different seasonal ARIMA models are proposed and calibrated on a rich dataset of 800 sound level measurements. The predictive capabilities of these techniques are encouraging. The implemented models show a good forecasting performances in terms of low residuals, i.e. difference between observed and estimated noise values. The residuals are analysed by means of statistical indexes, plots and tests.https://doi.org/10.1051/matecconf/201712505013 |
| spellingShingle | Guarnaccia Claudio Mastorakis Nikos E. Quartieri Joseph Tepedino Carmine Kaminaris Stavros D. Development of Seasonal ARIMA Models for Traffic Noise Forecasting MATEC Web of Conferences |
| title | Development of Seasonal ARIMA Models for Traffic Noise Forecasting |
| title_full | Development of Seasonal ARIMA Models for Traffic Noise Forecasting |
| title_fullStr | Development of Seasonal ARIMA Models for Traffic Noise Forecasting |
| title_full_unstemmed | Development of Seasonal ARIMA Models for Traffic Noise Forecasting |
| title_short | Development of Seasonal ARIMA Models for Traffic Noise Forecasting |
| title_sort | development of seasonal arima models for traffic noise forecasting |
| url | https://doi.org/10.1051/matecconf/201712505013 |
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