An adaptive nonlinear autoregressive ANN model for high time resolution traffic noise predictions. Experimental results for a port city waterfront

In this research study an adaptive recurrent artificial nonlinear neural network identification model has been developed and experimentally tested for dynamically predicting the traffic noise level L e q , 1 ′ with a time refinement of 1 min. The model has been successfully applied in three selected positions, representative of the waterfront in a Mediterranean port city. Several maritime cities are exposed to a wide range of road traffic fluctuations that negatively impact liveability in the area concerned. Large volumes of road traffic periodically access the port, dynamically affecting the acoustic scenario in neighbouring areas, especially in seaside towns during the tourist season. A signalized intersection, a roundabout, and a wide entrance to a vehicular underpass have been analyzed in the course of two characteristic periods, during which traffic ranged widely from normal to peak yearly intensity. Detailed traffic data for 15 road lanes and noise sequence regressors have been considered as input data sources. This exploratory investigation reveals a good predictive performance of the model developed, the prediction error of L e q , 1 ′ falling prevalently within the range ±0.5 dB. The experimental profile of L e q , 1 ′ is well reflected by the simulated sequence, and the auto and cross correlation functions confirm how well the identified neural model is able to explain the functional dependence underlying the experimental observations. Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Baccoli, Roberto [verfasserIn] Sollai, Federico [verfasserIn] Medda, Andrea [verfasserIn] Piccolo, Antonio [verfasserIn] Fadda, Paolo [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2021

Schlagwörter:	Traffic noise prediction model Dynamic model Nonlinear autoregressive neural network

Übergeordnetes Werk:	Enthalten in: Building and environment - New York, NY [u.a.] : Elsevier, 1976, 207
Übergeordnetes Werk:	volume:207

DOI / URN:	10.1016/j.buildenv.2021.108551

Katalog-ID:	ELV007106513

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650		4	\|a Traffic noise prediction model
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Indexfelder

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matchkey_str	article:03601323:2021----::ndpieolnaatrgesvanoefrihieeouinrfinierdcineprm
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bklnumber	56.00
publishDate	2021
allfields	10.1016/j.buildenv.2021.108551 doi (DE-627)ELV007106513 (ELSEVIER)S0360-1323(21)00944-6 DE-627 ger DE-627 rda eng 690 DE-600 56.00 bkl Baccoli, Roberto verfasserin aut An adaptive nonlinear autoregressive ANN model for high time resolution traffic noise predictions. Experimental results for a port city waterfront 2021 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In this research study an adaptive recurrent artificial nonlinear neural network identification model has been developed and experimentally tested for dynamically predicting the traffic noise level L e q , 1 ′ with a time refinement of 1 min. The model has been successfully applied in three selected positions, representative of the waterfront in a Mediterranean port city. Several maritime cities are exposed to a wide range of road traffic fluctuations that negatively impact liveability in the area concerned. Large volumes of road traffic periodically access the port, dynamically affecting the acoustic scenario in neighbouring areas, especially in seaside towns during the tourist season. A signalized intersection, a roundabout, and a wide entrance to a vehicular underpass have been analyzed in the course of two characteristic periods, during which traffic ranged widely from normal to peak yearly intensity. Detailed traffic data for 15 road lanes and noise sequence regressors have been considered as input data sources. This exploratory investigation reveals a good predictive performance of the model developed, the prediction error of L e q , 1 ′ falling prevalently within the range ±0.5 dB. The experimental profile of L e q , 1 ′ is well reflected by the simulated sequence, and the auto and cross correlation functions confirm how well the identified neural model is able to explain the functional dependence underlying the experimental observations. Traffic noise prediction model Dynamic model Nonlinear autoregressive neural network Sollai, Federico verfasserin aut Medda, Andrea verfasserin (orcid)0000-0003-3716-5274 aut Piccolo, Antonio verfasserin aut Fadda, Paolo verfasserin aut Enthalten in Building and environment New York, NY [u.a.] : Elsevier, 1976 207 Online-Ressource (DE-627)300188773 (DE-600)1481962-4 (DE-576)104402504 0360-1323 nnns volume:207 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 56.00 Bauwesen: Allgemeines AR 207
spelling	10.1016/j.buildenv.2021.108551 doi (DE-627)ELV007106513 (ELSEVIER)S0360-1323(21)00944-6 DE-627 ger DE-627 rda eng 690 DE-600 56.00 bkl Baccoli, Roberto verfasserin aut An adaptive nonlinear autoregressive ANN model for high time resolution traffic noise predictions. Experimental results for a port city waterfront 2021 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In this research study an adaptive recurrent artificial nonlinear neural network identification model has been developed and experimentally tested for dynamically predicting the traffic noise level L e q , 1 ′ with a time refinement of 1 min. The model has been successfully applied in three selected positions, representative of the waterfront in a Mediterranean port city. Several maritime cities are exposed to a wide range of road traffic fluctuations that negatively impact liveability in the area concerned. Large volumes of road traffic periodically access the port, dynamically affecting the acoustic scenario in neighbouring areas, especially in seaside towns during the tourist season. A signalized intersection, a roundabout, and a wide entrance to a vehicular underpass have been analyzed in the course of two characteristic periods, during which traffic ranged widely from normal to peak yearly intensity. Detailed traffic data for 15 road lanes and noise sequence regressors have been considered as input data sources. This exploratory investigation reveals a good predictive performance of the model developed, the prediction error of L e q , 1 ′ falling prevalently within the range ±0.5 dB. The experimental profile of L e q , 1 ′ is well reflected by the simulated sequence, and the auto and cross correlation functions confirm how well the identified neural model is able to explain the functional dependence underlying the experimental observations. Traffic noise prediction model Dynamic model Nonlinear autoregressive neural network Sollai, Federico verfasserin aut Medda, Andrea verfasserin (orcid)0000-0003-3716-5274 aut Piccolo, Antonio verfasserin aut Fadda, Paolo verfasserin aut Enthalten in Building and environment New York, NY [u.a.] : Elsevier, 1976 207 Online-Ressource (DE-627)300188773 (DE-600)1481962-4 (DE-576)104402504 0360-1323 nnns volume:207 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 56.00 Bauwesen: Allgemeines AR 207
allfields_unstemmed	10.1016/j.buildenv.2021.108551 doi (DE-627)ELV007106513 (ELSEVIER)S0360-1323(21)00944-6 DE-627 ger DE-627 rda eng 690 DE-600 56.00 bkl Baccoli, Roberto verfasserin aut An adaptive nonlinear autoregressive ANN model for high time resolution traffic noise predictions. Experimental results for a port city waterfront 2021 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In this research study an adaptive recurrent artificial nonlinear neural network identification model has been developed and experimentally tested for dynamically predicting the traffic noise level L e q , 1 ′ with a time refinement of 1 min. The model has been successfully applied in three selected positions, representative of the waterfront in a Mediterranean port city. Several maritime cities are exposed to a wide range of road traffic fluctuations that negatively impact liveability in the area concerned. Large volumes of road traffic periodically access the port, dynamically affecting the acoustic scenario in neighbouring areas, especially in seaside towns during the tourist season. A signalized intersection, a roundabout, and a wide entrance to a vehicular underpass have been analyzed in the course of two characteristic periods, during which traffic ranged widely from normal to peak yearly intensity. Detailed traffic data for 15 road lanes and noise sequence regressors have been considered as input data sources. This exploratory investigation reveals a good predictive performance of the model developed, the prediction error of L e q , 1 ′ falling prevalently within the range ±0.5 dB. The experimental profile of L e q , 1 ′ is well reflected by the simulated sequence, and the auto and cross correlation functions confirm how well the identified neural model is able to explain the functional dependence underlying the experimental observations. Traffic noise prediction model Dynamic model Nonlinear autoregressive neural network Sollai, Federico verfasserin aut Medda, Andrea verfasserin (orcid)0000-0003-3716-5274 aut Piccolo, Antonio verfasserin aut Fadda, Paolo verfasserin aut Enthalten in Building and environment New York, NY [u.a.] : Elsevier, 1976 207 Online-Ressource (DE-627)300188773 (DE-600)1481962-4 (DE-576)104402504 0360-1323 nnns volume:207 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 56.00 Bauwesen: Allgemeines AR 207
allfieldsGer	10.1016/j.buildenv.2021.108551 doi (DE-627)ELV007106513 (ELSEVIER)S0360-1323(21)00944-6 DE-627 ger DE-627 rda eng 690 DE-600 56.00 bkl Baccoli, Roberto verfasserin aut An adaptive nonlinear autoregressive ANN model for high time resolution traffic noise predictions. Experimental results for a port city waterfront 2021 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In this research study an adaptive recurrent artificial nonlinear neural network identification model has been developed and experimentally tested for dynamically predicting the traffic noise level L e q , 1 ′ with a time refinement of 1 min. The model has been successfully applied in three selected positions, representative of the waterfront in a Mediterranean port city. Several maritime cities are exposed to a wide range of road traffic fluctuations that negatively impact liveability in the area concerned. Large volumes of road traffic periodically access the port, dynamically affecting the acoustic scenario in neighbouring areas, especially in seaside towns during the tourist season. A signalized intersection, a roundabout, and a wide entrance to a vehicular underpass have been analyzed in the course of two characteristic periods, during which traffic ranged widely from normal to peak yearly intensity. Detailed traffic data for 15 road lanes and noise sequence regressors have been considered as input data sources. This exploratory investigation reveals a good predictive performance of the model developed, the prediction error of L e q , 1 ′ falling prevalently within the range ±0.5 dB. The experimental profile of L e q , 1 ′ is well reflected by the simulated sequence, and the auto and cross correlation functions confirm how well the identified neural model is able to explain the functional dependence underlying the experimental observations. Traffic noise prediction model Dynamic model Nonlinear autoregressive neural network Sollai, Federico verfasserin aut Medda, Andrea verfasserin (orcid)0000-0003-3716-5274 aut Piccolo, Antonio verfasserin aut Fadda, Paolo verfasserin aut Enthalten in Building and environment New York, NY [u.a.] : Elsevier, 1976 207 Online-Ressource (DE-627)300188773 (DE-600)1481962-4 (DE-576)104402504 0360-1323 nnns volume:207 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 56.00 Bauwesen: Allgemeines AR 207
allfieldsSound	10.1016/j.buildenv.2021.108551 doi (DE-627)ELV007106513 (ELSEVIER)S0360-1323(21)00944-6 DE-627 ger DE-627 rda eng 690 DE-600 56.00 bkl Baccoli, Roberto verfasserin aut An adaptive nonlinear autoregressive ANN model for high time resolution traffic noise predictions. Experimental results for a port city waterfront 2021 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In this research study an adaptive recurrent artificial nonlinear neural network identification model has been developed and experimentally tested for dynamically predicting the traffic noise level L e q , 1 ′ with a time refinement of 1 min. The model has been successfully applied in three selected positions, representative of the waterfront in a Mediterranean port city. Several maritime cities are exposed to a wide range of road traffic fluctuations that negatively impact liveability in the area concerned. Large volumes of road traffic periodically access the port, dynamically affecting the acoustic scenario in neighbouring areas, especially in seaside towns during the tourist season. A signalized intersection, a roundabout, and a wide entrance to a vehicular underpass have been analyzed in the course of two characteristic periods, during which traffic ranged widely from normal to peak yearly intensity. Detailed traffic data for 15 road lanes and noise sequence regressors have been considered as input data sources. This exploratory investigation reveals a good predictive performance of the model developed, the prediction error of L e q , 1 ′ falling prevalently within the range ±0.5 dB. The experimental profile of L e q , 1 ′ is well reflected by the simulated sequence, and the auto and cross correlation functions confirm how well the identified neural model is able to explain the functional dependence underlying the experimental observations. Traffic noise prediction model Dynamic model Nonlinear autoregressive neural network Sollai, Federico verfasserin aut Medda, Andrea verfasserin (orcid)0000-0003-3716-5274 aut Piccolo, Antonio verfasserin aut Fadda, Paolo verfasserin aut Enthalten in Building and environment New York, NY [u.a.] : Elsevier, 1976 207 Online-Ressource (DE-627)300188773 (DE-600)1481962-4 (DE-576)104402504 0360-1323 nnns volume:207 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 56.00 Bauwesen: Allgemeines AR 207
language	English
source	Enthalten in Building and environment 207 volume:207
sourceStr	Enthalten in Building and environment 207 volume:207
format_phy_str_mv	Article
bklname	Bauwesen: Allgemeines
institution	findex.gbv.de
topic_facet	Traffic noise prediction model Dynamic model Nonlinear autoregressive neural network
dewey-raw	690
isfreeaccess_bool	false
container_title	Building and environment
authorswithroles_txt_mv	Baccoli, Roberto @@aut@@ Sollai, Federico @@aut@@ Medda, Andrea @@aut@@ Piccolo, Antonio @@aut@@ Fadda, Paolo @@aut@@
publishDateDaySort_date	2021-01-01T00:00:00Z
hierarchy_top_id	300188773
dewey-sort	3690
id	ELV007106513
language_de	englisch
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author	Baccoli, Roberto
spellingShingle	Baccoli, Roberto ddc 690 bkl 56.00 misc Traffic noise prediction model misc Dynamic model misc Nonlinear autoregressive neural network An adaptive nonlinear autoregressive ANN model for high time resolution traffic noise predictions. Experimental results for a port city waterfront
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topic_title	690 DE-600 56.00 bkl An adaptive nonlinear autoregressive ANN model for high time resolution traffic noise predictions. Experimental results for a port city waterfront Traffic noise prediction model Dynamic model Nonlinear autoregressive neural network
topic	ddc 690 bkl 56.00 misc Traffic noise prediction model misc Dynamic model misc Nonlinear autoregressive neural network
topic_unstemmed	ddc 690 bkl 56.00 misc Traffic noise prediction model misc Dynamic model misc Nonlinear autoregressive neural network
topic_browse	ddc 690 bkl 56.00 misc Traffic noise prediction model misc Dynamic model misc Nonlinear autoregressive neural network
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title	An adaptive nonlinear autoregressive ANN model for high time resolution traffic noise predictions. Experimental results for a port city waterfront
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title_full	An adaptive nonlinear autoregressive ANN model for high time resolution traffic noise predictions. Experimental results for a port city waterfront
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author_browse	Baccoli, Roberto Sollai, Federico Medda, Andrea Piccolo, Antonio Fadda, Paolo
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title_sort	an adaptive nonlinear autoregressive ann model for high time resolution traffic noise predictions. experimental results for a port city waterfront
title_auth	An adaptive nonlinear autoregressive ANN model for high time resolution traffic noise predictions. Experimental results for a port city waterfront
abstract	In this research study an adaptive recurrent artificial nonlinear neural network identification model has been developed and experimentally tested for dynamically predicting the traffic noise level L e q , 1 ′ with a time refinement of 1 min. The model has been successfully applied in three selected positions, representative of the waterfront in a Mediterranean port city. Several maritime cities are exposed to a wide range of road traffic fluctuations that negatively impact liveability in the area concerned. Large volumes of road traffic periodically access the port, dynamically affecting the acoustic scenario in neighbouring areas, especially in seaside towns during the tourist season. A signalized intersection, a roundabout, and a wide entrance to a vehicular underpass have been analyzed in the course of two characteristic periods, during which traffic ranged widely from normal to peak yearly intensity. Detailed traffic data for 15 road lanes and noise sequence regressors have been considered as input data sources. This exploratory investigation reveals a good predictive performance of the model developed, the prediction error of L e q , 1 ′ falling prevalently within the range ±0.5 dB. The experimental profile of L e q , 1 ′ is well reflected by the simulated sequence, and the auto and cross correlation functions confirm how well the identified neural model is able to explain the functional dependence underlying the experimental observations.
abstractGer	In this research study an adaptive recurrent artificial nonlinear neural network identification model has been developed and experimentally tested for dynamically predicting the traffic noise level L e q , 1 ′ with a time refinement of 1 min. The model has been successfully applied in three selected positions, representative of the waterfront in a Mediterranean port city. Several maritime cities are exposed to a wide range of road traffic fluctuations that negatively impact liveability in the area concerned. Large volumes of road traffic periodically access the port, dynamically affecting the acoustic scenario in neighbouring areas, especially in seaside towns during the tourist season. A signalized intersection, a roundabout, and a wide entrance to a vehicular underpass have been analyzed in the course of two characteristic periods, during which traffic ranged widely from normal to peak yearly intensity. Detailed traffic data for 15 road lanes and noise sequence regressors have been considered as input data sources. This exploratory investigation reveals a good predictive performance of the model developed, the prediction error of L e q , 1 ′ falling prevalently within the range ±0.5 dB. The experimental profile of L e q , 1 ′ is well reflected by the simulated sequence, and the auto and cross correlation functions confirm how well the identified neural model is able to explain the functional dependence underlying the experimental observations.
abstract_unstemmed	In this research study an adaptive recurrent artificial nonlinear neural network identification model has been developed and experimentally tested for dynamically predicting the traffic noise level L e q , 1 ′ with a time refinement of 1 min. The model has been successfully applied in three selected positions, representative of the waterfront in a Mediterranean port city. Several maritime cities are exposed to a wide range of road traffic fluctuations that negatively impact liveability in the area concerned. Large volumes of road traffic periodically access the port, dynamically affecting the acoustic scenario in neighbouring areas, especially in seaside towns during the tourist season. A signalized intersection, a roundabout, and a wide entrance to a vehicular underpass have been analyzed in the course of two characteristic periods, during which traffic ranged widely from normal to peak yearly intensity. Detailed traffic data for 15 road lanes and noise sequence regressors have been considered as input data sources. This exploratory investigation reveals a good predictive performance of the model developed, the prediction error of L e q , 1 ′ falling prevalently within the range ±0.5 dB. The experimental profile of L e q , 1 ′ is well reflected by the simulated sequence, and the auto and cross correlation functions confirm how well the identified neural model is able to explain the functional dependence underlying the experimental observations.
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title_short	An adaptive nonlinear autoregressive ANN model for high time resolution traffic noise predictions. Experimental results for a port city waterfront
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author2	Sollai, Federico Medda, Andrea Piccolo, Antonio Fadda, Paolo
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up_date	2024-07-06T23:38:16.146Z
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An adaptive nonlinear autoregressive ANN model for high time resolution traffic noise predictions. Experimental results for a port city waterfront

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