MAPredRNN: multi-attention predictive RNN for traffic flow prediction by dynamic spatio-temporal data fusion

Abstract Traffic flow prediction is a key component of intelligent transportation system, especially for increasingly complex urban traffic networks. An accurate flow prediction can help to relieve traffic congestion and reduce traffic accidents. However, the patterns of traffic flow are very comple...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Huang, Xiaohui [verfasserIn] Jiang, Yuan Tang, Jie

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	Multi-attention Traffic flow prediction Spatio-temporal

Anmerkung:	© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Übergeordnetes Werk:	Enthalten in: Applied intelligence - Dordrecht [u.a.] : Springer Science + Business Media B.V, 1991, 53(2023), 16 vom: 06. März, Seite 19372-19383
Übergeordnetes Werk:	volume:53 ; year:2023 ; number:16 ; day:06 ; month:03 ; pages:19372-19383

Links:	Volltext

DOI / URN:	10.1007/s10489-023-04494-8

Katalog-ID:	SPR052895866

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520			\|a Abstract Traffic flow prediction is a key component of intelligent transportation system, especially for increasingly complex urban traffic networks. An accurate flow prediction can help to relieve traffic congestion and reduce traffic accidents. However, the patterns of traffic flow are very complex and volatile, which will be affected by many factors, such as traffic accident, weather, point-of-interests, etc. It is still a challenging issue due to the high nonlinearity and dynamicity of traffic flow. In this paper, we propose a multi-attention predictive recurrent neural networks (MAPredRNN) for traffic flow prediction by dynamic spatio-temporal data fusion. First, convolutional neural network and predictive recurrent neural network are used to obtain the spatio-temporal information of the closeness, periodicity and trend features. And then, multi-attention mechanism is employed to further extract feature fusing information of closeness, periodicity and trend. Experimental results conducted on two real datasets show that our proposed method outperforms the compared algorithms.
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allfields	10.1007/s10489-023-04494-8 doi (DE-627)SPR052895866 (SPR)s10489-023-04494-8-e DE-627 ger DE-627 rakwb eng Huang, Xiaohui verfasserin aut MAPredRNN: multi-attention predictive RNN for traffic flow prediction by dynamic spatio-temporal data fusion 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Traffic flow prediction is a key component of intelligent transportation system, especially for increasingly complex urban traffic networks. An accurate flow prediction can help to relieve traffic congestion and reduce traffic accidents. However, the patterns of traffic flow are very complex and volatile, which will be affected by many factors, such as traffic accident, weather, point-of-interests, etc. It is still a challenging issue due to the high nonlinearity and dynamicity of traffic flow. In this paper, we propose a multi-attention predictive recurrent neural networks (MAPredRNN) for traffic flow prediction by dynamic spatio-temporal data fusion. First, convolutional neural network and predictive recurrent neural network are used to obtain the spatio-temporal information of the closeness, periodicity and trend features. And then, multi-attention mechanism is employed to further extract feature fusing information of closeness, periodicity and trend. Experimental results conducted on two real datasets show that our proposed method outperforms the compared algorithms. Multi-attention (dpeaa)DE-He213 Traffic flow prediction (dpeaa)DE-He213 Spatio-temporal (dpeaa)DE-He213 Jiang, Yuan (orcid)0000-0003-3266-172X aut Tang, Jie aut Enthalten in Applied intelligence Dordrecht [u.a.] : Springer Science + Business Media B.V, 1991 53(2023), 16 vom: 06. März, Seite 19372-19383 (DE-627)271180919 (DE-600)1479519-X 1573-7497 nnns volume:53 year:2023 number:16 day:06 month:03 pages:19372-19383 https://dx.doi.org/10.1007/s10489-023-04494-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 53 2023 16 06 03 19372-19383
spelling	10.1007/s10489-023-04494-8 doi (DE-627)SPR052895866 (SPR)s10489-023-04494-8-e DE-627 ger DE-627 rakwb eng Huang, Xiaohui verfasserin aut MAPredRNN: multi-attention predictive RNN for traffic flow prediction by dynamic spatio-temporal data fusion 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Traffic flow prediction is a key component of intelligent transportation system, especially for increasingly complex urban traffic networks. An accurate flow prediction can help to relieve traffic congestion and reduce traffic accidents. However, the patterns of traffic flow are very complex and volatile, which will be affected by many factors, such as traffic accident, weather, point-of-interests, etc. It is still a challenging issue due to the high nonlinearity and dynamicity of traffic flow. In this paper, we propose a multi-attention predictive recurrent neural networks (MAPredRNN) for traffic flow prediction by dynamic spatio-temporal data fusion. First, convolutional neural network and predictive recurrent neural network are used to obtain the spatio-temporal information of the closeness, periodicity and trend features. And then, multi-attention mechanism is employed to further extract feature fusing information of closeness, periodicity and trend. Experimental results conducted on two real datasets show that our proposed method outperforms the compared algorithms. Multi-attention (dpeaa)DE-He213 Traffic flow prediction (dpeaa)DE-He213 Spatio-temporal (dpeaa)DE-He213 Jiang, Yuan (orcid)0000-0003-3266-172X aut Tang, Jie aut Enthalten in Applied intelligence Dordrecht [u.a.] : Springer Science + Business Media B.V, 1991 53(2023), 16 vom: 06. März, Seite 19372-19383 (DE-627)271180919 (DE-600)1479519-X 1573-7497 nnns volume:53 year:2023 number:16 day:06 month:03 pages:19372-19383 https://dx.doi.org/10.1007/s10489-023-04494-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 53 2023 16 06 03 19372-19383
allfields_unstemmed	10.1007/s10489-023-04494-8 doi (DE-627)SPR052895866 (SPR)s10489-023-04494-8-e DE-627 ger DE-627 rakwb eng Huang, Xiaohui verfasserin aut MAPredRNN: multi-attention predictive RNN for traffic flow prediction by dynamic spatio-temporal data fusion 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Traffic flow prediction is a key component of intelligent transportation system, especially for increasingly complex urban traffic networks. An accurate flow prediction can help to relieve traffic congestion and reduce traffic accidents. However, the patterns of traffic flow are very complex and volatile, which will be affected by many factors, such as traffic accident, weather, point-of-interests, etc. It is still a challenging issue due to the high nonlinearity and dynamicity of traffic flow. In this paper, we propose a multi-attention predictive recurrent neural networks (MAPredRNN) for traffic flow prediction by dynamic spatio-temporal data fusion. First, convolutional neural network and predictive recurrent neural network are used to obtain the spatio-temporal information of the closeness, periodicity and trend features. And then, multi-attention mechanism is employed to further extract feature fusing information of closeness, periodicity and trend. Experimental results conducted on two real datasets show that our proposed method outperforms the compared algorithms. Multi-attention (dpeaa)DE-He213 Traffic flow prediction (dpeaa)DE-He213 Spatio-temporal (dpeaa)DE-He213 Jiang, Yuan (orcid)0000-0003-3266-172X aut Tang, Jie aut Enthalten in Applied intelligence Dordrecht [u.a.] : Springer Science + Business Media B.V, 1991 53(2023), 16 vom: 06. März, Seite 19372-19383 (DE-627)271180919 (DE-600)1479519-X 1573-7497 nnns volume:53 year:2023 number:16 day:06 month:03 pages:19372-19383 https://dx.doi.org/10.1007/s10489-023-04494-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 53 2023 16 06 03 19372-19383
allfieldsGer	10.1007/s10489-023-04494-8 doi (DE-627)SPR052895866 (SPR)s10489-023-04494-8-e DE-627 ger DE-627 rakwb eng Huang, Xiaohui verfasserin aut MAPredRNN: multi-attention predictive RNN for traffic flow prediction by dynamic spatio-temporal data fusion 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Traffic flow prediction is a key component of intelligent transportation system, especially for increasingly complex urban traffic networks. An accurate flow prediction can help to relieve traffic congestion and reduce traffic accidents. However, the patterns of traffic flow are very complex and volatile, which will be affected by many factors, such as traffic accident, weather, point-of-interests, etc. It is still a challenging issue due to the high nonlinearity and dynamicity of traffic flow. In this paper, we propose a multi-attention predictive recurrent neural networks (MAPredRNN) for traffic flow prediction by dynamic spatio-temporal data fusion. First, convolutional neural network and predictive recurrent neural network are used to obtain the spatio-temporal information of the closeness, periodicity and trend features. And then, multi-attention mechanism is employed to further extract feature fusing information of closeness, periodicity and trend. Experimental results conducted on two real datasets show that our proposed method outperforms the compared algorithms. Multi-attention (dpeaa)DE-He213 Traffic flow prediction (dpeaa)DE-He213 Spatio-temporal (dpeaa)DE-He213 Jiang, Yuan (orcid)0000-0003-3266-172X aut Tang, Jie aut Enthalten in Applied intelligence Dordrecht [u.a.] : Springer Science + Business Media B.V, 1991 53(2023), 16 vom: 06. März, Seite 19372-19383 (DE-627)271180919 (DE-600)1479519-X 1573-7497 nnns volume:53 year:2023 number:16 day:06 month:03 pages:19372-19383 https://dx.doi.org/10.1007/s10489-023-04494-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 53 2023 16 06 03 19372-19383
allfieldsSound	10.1007/s10489-023-04494-8 doi (DE-627)SPR052895866 (SPR)s10489-023-04494-8-e DE-627 ger DE-627 rakwb eng Huang, Xiaohui verfasserin aut MAPredRNN: multi-attention predictive RNN for traffic flow prediction by dynamic spatio-temporal data fusion 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Traffic flow prediction is a key component of intelligent transportation system, especially for increasingly complex urban traffic networks. An accurate flow prediction can help to relieve traffic congestion and reduce traffic accidents. However, the patterns of traffic flow are very complex and volatile, which will be affected by many factors, such as traffic accident, weather, point-of-interests, etc. It is still a challenging issue due to the high nonlinearity and dynamicity of traffic flow. In this paper, we propose a multi-attention predictive recurrent neural networks (MAPredRNN) for traffic flow prediction by dynamic spatio-temporal data fusion. First, convolutional neural network and predictive recurrent neural network are used to obtain the spatio-temporal information of the closeness, periodicity and trend features. And then, multi-attention mechanism is employed to further extract feature fusing information of closeness, periodicity and trend. Experimental results conducted on two real datasets show that our proposed method outperforms the compared algorithms. Multi-attention (dpeaa)DE-He213 Traffic flow prediction (dpeaa)DE-He213 Spatio-temporal (dpeaa)DE-He213 Jiang, Yuan (orcid)0000-0003-3266-172X aut Tang, Jie aut Enthalten in Applied intelligence Dordrecht [u.a.] : Springer Science + Business Media B.V, 1991 53(2023), 16 vom: 06. März, Seite 19372-19383 (DE-627)271180919 (DE-600)1479519-X 1573-7497 nnns volume:53 year:2023 number:16 day:06 month:03 pages:19372-19383 https://dx.doi.org/10.1007/s10489-023-04494-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 53 2023 16 06 03 19372-19383
language	English
source	Enthalten in Applied intelligence 53(2023), 16 vom: 06. März, Seite 19372-19383 volume:53 year:2023 number:16 day:06 month:03 pages:19372-19383
sourceStr	Enthalten in Applied intelligence 53(2023), 16 vom: 06. März, Seite 19372-19383 volume:53 year:2023 number:16 day:06 month:03 pages:19372-19383
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topic_facet	Multi-attention Traffic flow prediction Spatio-temporal
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author	Huang, Xiaohui
spellingShingle	Huang, Xiaohui misc Multi-attention misc Traffic flow prediction misc Spatio-temporal MAPredRNN: multi-attention predictive RNN for traffic flow prediction by dynamic spatio-temporal data fusion
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title_sort	mapredrnn: multi-attention predictive rnn for traffic flow prediction by dynamic spatio-temporal data fusion
title_auth	MAPredRNN: multi-attention predictive RNN for traffic flow prediction by dynamic spatio-temporal data fusion
abstract	Abstract Traffic flow prediction is a key component of intelligent transportation system, especially for increasingly complex urban traffic networks. An accurate flow prediction can help to relieve traffic congestion and reduce traffic accidents. However, the patterns of traffic flow are very complex and volatile, which will be affected by many factors, such as traffic accident, weather, point-of-interests, etc. It is still a challenging issue due to the high nonlinearity and dynamicity of traffic flow. In this paper, we propose a multi-attention predictive recurrent neural networks (MAPredRNN) for traffic flow prediction by dynamic spatio-temporal data fusion. First, convolutional neural network and predictive recurrent neural network are used to obtain the spatio-temporal information of the closeness, periodicity and trend features. And then, multi-attention mechanism is employed to further extract feature fusing information of closeness, periodicity and trend. Experimental results conducted on two real datasets show that our proposed method outperforms the compared algorithms. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
abstractGer	Abstract Traffic flow prediction is a key component of intelligent transportation system, especially for increasingly complex urban traffic networks. An accurate flow prediction can help to relieve traffic congestion and reduce traffic accidents. However, the patterns of traffic flow are very complex and volatile, which will be affected by many factors, such as traffic accident, weather, point-of-interests, etc. It is still a challenging issue due to the high nonlinearity and dynamicity of traffic flow. In this paper, we propose a multi-attention predictive recurrent neural networks (MAPredRNN) for traffic flow prediction by dynamic spatio-temporal data fusion. First, convolutional neural network and predictive recurrent neural network are used to obtain the spatio-temporal information of the closeness, periodicity and trend features. And then, multi-attention mechanism is employed to further extract feature fusing information of closeness, periodicity and trend. Experimental results conducted on two real datasets show that our proposed method outperforms the compared algorithms. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
abstract_unstemmed	Abstract Traffic flow prediction is a key component of intelligent transportation system, especially for increasingly complex urban traffic networks. An accurate flow prediction can help to relieve traffic congestion and reduce traffic accidents. However, the patterns of traffic flow are very complex and volatile, which will be affected by many factors, such as traffic accident, weather, point-of-interests, etc. It is still a challenging issue due to the high nonlinearity and dynamicity of traffic flow. In this paper, we propose a multi-attention predictive recurrent neural networks (MAPredRNN) for traffic flow prediction by dynamic spatio-temporal data fusion. First, convolutional neural network and predictive recurrent neural network are used to obtain the spatio-temporal information of the closeness, periodicity and trend features. And then, multi-attention mechanism is employed to further extract feature fusing information of closeness, periodicity and trend. Experimental results conducted on two real datasets show that our proposed method outperforms the compared algorithms. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
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container_issue	16
title_short	MAPredRNN: multi-attention predictive RNN for traffic flow prediction by dynamic spatio-temporal data fusion
url	https://dx.doi.org/10.1007/s10489-023-04494-8
remote_bool	true
author2	Jiang, Yuan Tang, Jie
author2Str	Jiang, Yuan Tang, Jie
ppnlink	271180919
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doi_str	10.1007/s10489-023-04494-8
up_date	2024-07-03T15:34:30.067Z
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