Attentive differential convolutional neural networks for crowd flow prediction
Traffic crowd flow prediction has drawn more attention in both academic and industry communities due to the explosive growth of traffic data. Generally, existing studies focused on either using as many factors as possible or improving the model structure for describing the spatio-temporal dependence...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Mo, Jiqian [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2022transfer abstract |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Subsurface fluid flow at an active cold seep area in the Qiongdongnan Basin, northern South China Sea - Wang, Jiliang ELSEVIER, 2018, Amsterdam [u.a.] |
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| Übergeordnetes Werk: |
volume:258 ; year:2022 ; day:22 ; month:12 ; pages:0 |
| Links: |
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| DOI / URN: |
10.1016/j.knosys.2022.110006 |
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ELV05949350X |
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| 520 | |a Traffic crowd flow prediction has drawn more attention in both academic and industry communities due to the explosive growth of traffic data. Generally, existing studies focused on either using as many factors as possible or improving the model structure for describing the spatio-temporal dependence of data. However, due to the complexity and diversity of these influencing factors, few of them can successfully capture the spatial correlation and temporal dependence at the same time. Moreover, none of them have considered the high-order spatio-temporal dependence of traffic data. In fact, mathematically the development of a state can be well described by its current value, together with the series of its high order changing rate. For this insight, in this paper we propose a CNN-based architecture, called Attentive Differential Convolutional Neural Network (ADCNN), to encode the current state and its high order changing rate using the historical traffic data. Inspired by the idea of that a function can be approximated by using a finite number of terms of its Taylor expansion, we design a cascade architecture based on the Hierarchical Differential Unit (HDU) and Cross-Attention mechanism which can learn the high order changing rate of the current state. We conduct extensive experiments to evaluate ADCNN on three real-world traffic datasets. Experimental results show that ADCNN outperforms the other state-of-the-art models. | ||
| 520 | |a Traffic crowd flow prediction has drawn more attention in both academic and industry communities due to the explosive growth of traffic data. Generally, existing studies focused on either using as many factors as possible or improving the model structure for describing the spatio-temporal dependence of data. However, due to the complexity and diversity of these influencing factors, few of them can successfully capture the spatial correlation and temporal dependence at the same time. Moreover, none of them have considered the high-order spatio-temporal dependence of traffic data. In fact, mathematically the development of a state can be well described by its current value, together with the series of its high order changing rate. For this insight, in this paper we propose a CNN-based architecture, called Attentive Differential Convolutional Neural Network (ADCNN), to encode the current state and its high order changing rate using the historical traffic data. Inspired by the idea of that a function can be approximated by using a finite number of terms of its Taylor expansion, we design a cascade architecture based on the Hierarchical Differential Unit (HDU) and Cross-Attention mechanism which can learn the high order changing rate of the current state. We conduct extensive experiments to evaluate ADCNN on three real-world traffic datasets. Experimental results show that ADCNN outperforms the other state-of-the-art models. | ||
| 650 | 7 | |a Crowd flow prediction |2 Elsevier | |
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| 700 | 1 | |a Chen, Junyang |4 oth | |
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10.1016/j.knosys.2022.110006 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001972.pica (DE-627)ELV05949350X (ELSEVIER)S0950-7051(22)01099-1 DE-627 ger DE-627 rakwb eng 550 VZ 38.00 bkl Mo, Jiqian verfasserin aut Attentive differential convolutional neural networks for crowd flow prediction 2022transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Traffic crowd flow prediction has drawn more attention in both academic and industry communities due to the explosive growth of traffic data. Generally, existing studies focused on either using as many factors as possible or improving the model structure for describing the spatio-temporal dependence of data. However, due to the complexity and diversity of these influencing factors, few of them can successfully capture the spatial correlation and temporal dependence at the same time. Moreover, none of them have considered the high-order spatio-temporal dependence of traffic data. In fact, mathematically the development of a state can be well described by its current value, together with the series of its high order changing rate. For this insight, in this paper we propose a CNN-based architecture, called Attentive Differential Convolutional Neural Network (ADCNN), to encode the current state and its high order changing rate using the historical traffic data. Inspired by the idea of that a function can be approximated by using a finite number of terms of its Taylor expansion, we design a cascade architecture based on the Hierarchical Differential Unit (HDU) and Cross-Attention mechanism which can learn the high order changing rate of the current state. We conduct extensive experiments to evaluate ADCNN on three real-world traffic datasets. Experimental results show that ADCNN outperforms the other state-of-the-art models. Traffic crowd flow prediction has drawn more attention in both academic and industry communities due to the explosive growth of traffic data. Generally, existing studies focused on either using as many factors as possible or improving the model structure for describing the spatio-temporal dependence of data. However, due to the complexity and diversity of these influencing factors, few of them can successfully capture the spatial correlation and temporal dependence at the same time. Moreover, none of them have considered the high-order spatio-temporal dependence of traffic data. In fact, mathematically the development of a state can be well described by its current value, together with the series of its high order changing rate. For this insight, in this paper we propose a CNN-based architecture, called Attentive Differential Convolutional Neural Network (ADCNN), to encode the current state and its high order changing rate using the historical traffic data. Inspired by the idea of that a function can be approximated by using a finite number of terms of its Taylor expansion, we design a cascade architecture based on the Hierarchical Differential Unit (HDU) and Cross-Attention mechanism which can learn the high order changing rate of the current state. We conduct extensive experiments to evaluate ADCNN on three real-world traffic datasets. Experimental results show that ADCNN outperforms the other state-of-the-art models. Crowd flow prediction Elsevier CNN Elsevier Attention Elsevier Taylor expansion Elsevier Gong, Zhiguo oth Chen, Junyang oth Enthalten in Elsevier Science Wang, Jiliang ELSEVIER Subsurface fluid flow at an active cold seep area in the Qiongdongnan Basin, northern South China Sea 2018 Amsterdam [u.a.] (DE-627)ELV001104926 volume:258 year:2022 day:22 month:12 pages:0 https://doi.org/10.1016/j.knosys.2022.110006 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OPC-GGO 38.00 Geowissenschaften: Allgemeines VZ AR 258 2022 22 1222 0 |
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10.1016/j.knosys.2022.110006 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001972.pica (DE-627)ELV05949350X (ELSEVIER)S0950-7051(22)01099-1 DE-627 ger DE-627 rakwb eng 550 VZ 38.00 bkl Mo, Jiqian verfasserin aut Attentive differential convolutional neural networks for crowd flow prediction 2022transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Traffic crowd flow prediction has drawn more attention in both academic and industry communities due to the explosive growth of traffic data. Generally, existing studies focused on either using as many factors as possible or improving the model structure for describing the spatio-temporal dependence of data. However, due to the complexity and diversity of these influencing factors, few of them can successfully capture the spatial correlation and temporal dependence at the same time. Moreover, none of them have considered the high-order spatio-temporal dependence of traffic data. In fact, mathematically the development of a state can be well described by its current value, together with the series of its high order changing rate. For this insight, in this paper we propose a CNN-based architecture, called Attentive Differential Convolutional Neural Network (ADCNN), to encode the current state and its high order changing rate using the historical traffic data. Inspired by the idea of that a function can be approximated by using a finite number of terms of its Taylor expansion, we design a cascade architecture based on the Hierarchical Differential Unit (HDU) and Cross-Attention mechanism which can learn the high order changing rate of the current state. We conduct extensive experiments to evaluate ADCNN on three real-world traffic datasets. Experimental results show that ADCNN outperforms the other state-of-the-art models. Traffic crowd flow prediction has drawn more attention in both academic and industry communities due to the explosive growth of traffic data. Generally, existing studies focused on either using as many factors as possible or improving the model structure for describing the spatio-temporal dependence of data. However, due to the complexity and diversity of these influencing factors, few of them can successfully capture the spatial correlation and temporal dependence at the same time. Moreover, none of them have considered the high-order spatio-temporal dependence of traffic data. In fact, mathematically the development of a state can be well described by its current value, together with the series of its high order changing rate. For this insight, in this paper we propose a CNN-based architecture, called Attentive Differential Convolutional Neural Network (ADCNN), to encode the current state and its high order changing rate using the historical traffic data. Inspired by the idea of that a function can be approximated by using a finite number of terms of its Taylor expansion, we design a cascade architecture based on the Hierarchical Differential Unit (HDU) and Cross-Attention mechanism which can learn the high order changing rate of the current state. We conduct extensive experiments to evaluate ADCNN on three real-world traffic datasets. Experimental results show that ADCNN outperforms the other state-of-the-art models. Crowd flow prediction Elsevier CNN Elsevier Attention Elsevier Taylor expansion Elsevier Gong, Zhiguo oth Chen, Junyang oth Enthalten in Elsevier Science Wang, Jiliang ELSEVIER Subsurface fluid flow at an active cold seep area in the Qiongdongnan Basin, northern South China Sea 2018 Amsterdam [u.a.] (DE-627)ELV001104926 volume:258 year:2022 day:22 month:12 pages:0 https://doi.org/10.1016/j.knosys.2022.110006 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OPC-GGO 38.00 Geowissenschaften: Allgemeines VZ AR 258 2022 22 1222 0 |
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10.1016/j.knosys.2022.110006 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001972.pica (DE-627)ELV05949350X (ELSEVIER)S0950-7051(22)01099-1 DE-627 ger DE-627 rakwb eng 550 VZ 38.00 bkl Mo, Jiqian verfasserin aut Attentive differential convolutional neural networks for crowd flow prediction 2022transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Traffic crowd flow prediction has drawn more attention in both academic and industry communities due to the explosive growth of traffic data. Generally, existing studies focused on either using as many factors as possible or improving the model structure for describing the spatio-temporal dependence of data. However, due to the complexity and diversity of these influencing factors, few of them can successfully capture the spatial correlation and temporal dependence at the same time. Moreover, none of them have considered the high-order spatio-temporal dependence of traffic data. In fact, mathematically the development of a state can be well described by its current value, together with the series of its high order changing rate. For this insight, in this paper we propose a CNN-based architecture, called Attentive Differential Convolutional Neural Network (ADCNN), to encode the current state and its high order changing rate using the historical traffic data. Inspired by the idea of that a function can be approximated by using a finite number of terms of its Taylor expansion, we design a cascade architecture based on the Hierarchical Differential Unit (HDU) and Cross-Attention mechanism which can learn the high order changing rate of the current state. We conduct extensive experiments to evaluate ADCNN on three real-world traffic datasets. Experimental results show that ADCNN outperforms the other state-of-the-art models. Traffic crowd flow prediction has drawn more attention in both academic and industry communities due to the explosive growth of traffic data. Generally, existing studies focused on either using as many factors as possible or improving the model structure for describing the spatio-temporal dependence of data. However, due to the complexity and diversity of these influencing factors, few of them can successfully capture the spatial correlation and temporal dependence at the same time. Moreover, none of them have considered the high-order spatio-temporal dependence of traffic data. In fact, mathematically the development of a state can be well described by its current value, together with the series of its high order changing rate. For this insight, in this paper we propose a CNN-based architecture, called Attentive Differential Convolutional Neural Network (ADCNN), to encode the current state and its high order changing rate using the historical traffic data. Inspired by the idea of that a function can be approximated by using a finite number of terms of its Taylor expansion, we design a cascade architecture based on the Hierarchical Differential Unit (HDU) and Cross-Attention mechanism which can learn the high order changing rate of the current state. We conduct extensive experiments to evaluate ADCNN on three real-world traffic datasets. Experimental results show that ADCNN outperforms the other state-of-the-art models. Crowd flow prediction Elsevier CNN Elsevier Attention Elsevier Taylor expansion Elsevier Gong, Zhiguo oth Chen, Junyang oth Enthalten in Elsevier Science Wang, Jiliang ELSEVIER Subsurface fluid flow at an active cold seep area in the Qiongdongnan Basin, northern South China Sea 2018 Amsterdam [u.a.] (DE-627)ELV001104926 volume:258 year:2022 day:22 month:12 pages:0 https://doi.org/10.1016/j.knosys.2022.110006 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OPC-GGO 38.00 Geowissenschaften: Allgemeines VZ AR 258 2022 22 1222 0 |
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10.1016/j.knosys.2022.110006 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001972.pica (DE-627)ELV05949350X (ELSEVIER)S0950-7051(22)01099-1 DE-627 ger DE-627 rakwb eng 550 VZ 38.00 bkl Mo, Jiqian verfasserin aut Attentive differential convolutional neural networks for crowd flow prediction 2022transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Traffic crowd flow prediction has drawn more attention in both academic and industry communities due to the explosive growth of traffic data. Generally, existing studies focused on either using as many factors as possible or improving the model structure for describing the spatio-temporal dependence of data. However, due to the complexity and diversity of these influencing factors, few of them can successfully capture the spatial correlation and temporal dependence at the same time. Moreover, none of them have considered the high-order spatio-temporal dependence of traffic data. In fact, mathematically the development of a state can be well described by its current value, together with the series of its high order changing rate. For this insight, in this paper we propose a CNN-based architecture, called Attentive Differential Convolutional Neural Network (ADCNN), to encode the current state and its high order changing rate using the historical traffic data. Inspired by the idea of that a function can be approximated by using a finite number of terms of its Taylor expansion, we design a cascade architecture based on the Hierarchical Differential Unit (HDU) and Cross-Attention mechanism which can learn the high order changing rate of the current state. We conduct extensive experiments to evaluate ADCNN on three real-world traffic datasets. Experimental results show that ADCNN outperforms the other state-of-the-art models. Traffic crowd flow prediction has drawn more attention in both academic and industry communities due to the explosive growth of traffic data. Generally, existing studies focused on either using as many factors as possible or improving the model structure for describing the spatio-temporal dependence of data. However, due to the complexity and diversity of these influencing factors, few of them can successfully capture the spatial correlation and temporal dependence at the same time. Moreover, none of them have considered the high-order spatio-temporal dependence of traffic data. In fact, mathematically the development of a state can be well described by its current value, together with the series of its high order changing rate. For this insight, in this paper we propose a CNN-based architecture, called Attentive Differential Convolutional Neural Network (ADCNN), to encode the current state and its high order changing rate using the historical traffic data. Inspired by the idea of that a function can be approximated by using a finite number of terms of its Taylor expansion, we design a cascade architecture based on the Hierarchical Differential Unit (HDU) and Cross-Attention mechanism which can learn the high order changing rate of the current state. We conduct extensive experiments to evaluate ADCNN on three real-world traffic datasets. Experimental results show that ADCNN outperforms the other state-of-the-art models. Crowd flow prediction Elsevier CNN Elsevier Attention Elsevier Taylor expansion Elsevier Gong, Zhiguo oth Chen, Junyang oth Enthalten in Elsevier Science Wang, Jiliang ELSEVIER Subsurface fluid flow at an active cold seep area in the Qiongdongnan Basin, northern South China Sea 2018 Amsterdam [u.a.] (DE-627)ELV001104926 volume:258 year:2022 day:22 month:12 pages:0 https://doi.org/10.1016/j.knosys.2022.110006 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OPC-GGO 38.00 Geowissenschaften: Allgemeines VZ AR 258 2022 22 1222 0 |
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10.1016/j.knosys.2022.110006 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001972.pica (DE-627)ELV05949350X (ELSEVIER)S0950-7051(22)01099-1 DE-627 ger DE-627 rakwb eng 550 VZ 38.00 bkl Mo, Jiqian verfasserin aut Attentive differential convolutional neural networks for crowd flow prediction 2022transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Traffic crowd flow prediction has drawn more attention in both academic and industry communities due to the explosive growth of traffic data. Generally, existing studies focused on either using as many factors as possible or improving the model structure for describing the spatio-temporal dependence of data. However, due to the complexity and diversity of these influencing factors, few of them can successfully capture the spatial correlation and temporal dependence at the same time. Moreover, none of them have considered the high-order spatio-temporal dependence of traffic data. In fact, mathematically the development of a state can be well described by its current value, together with the series of its high order changing rate. For this insight, in this paper we propose a CNN-based architecture, called Attentive Differential Convolutional Neural Network (ADCNN), to encode the current state and its high order changing rate using the historical traffic data. Inspired by the idea of that a function can be approximated by using a finite number of terms of its Taylor expansion, we design a cascade architecture based on the Hierarchical Differential Unit (HDU) and Cross-Attention mechanism which can learn the high order changing rate of the current state. We conduct extensive experiments to evaluate ADCNN on three real-world traffic datasets. Experimental results show that ADCNN outperforms the other state-of-the-art models. Traffic crowd flow prediction has drawn more attention in both academic and industry communities due to the explosive growth of traffic data. Generally, existing studies focused on either using as many factors as possible or improving the model structure for describing the spatio-temporal dependence of data. However, due to the complexity and diversity of these influencing factors, few of them can successfully capture the spatial correlation and temporal dependence at the same time. Moreover, none of them have considered the high-order spatio-temporal dependence of traffic data. In fact, mathematically the development of a state can be well described by its current value, together with the series of its high order changing rate. For this insight, in this paper we propose a CNN-based architecture, called Attentive Differential Convolutional Neural Network (ADCNN), to encode the current state and its high order changing rate using the historical traffic data. Inspired by the idea of that a function can be approximated by using a finite number of terms of its Taylor expansion, we design a cascade architecture based on the Hierarchical Differential Unit (HDU) and Cross-Attention mechanism which can learn the high order changing rate of the current state. We conduct extensive experiments to evaluate ADCNN on three real-world traffic datasets. Experimental results show that ADCNN outperforms the other state-of-the-art models. Crowd flow prediction Elsevier CNN Elsevier Attention Elsevier Taylor expansion Elsevier Gong, Zhiguo oth Chen, Junyang oth Enthalten in Elsevier Science Wang, Jiliang ELSEVIER Subsurface fluid flow at an active cold seep area in the Qiongdongnan Basin, northern South China Sea 2018 Amsterdam [u.a.] (DE-627)ELV001104926 volume:258 year:2022 day:22 month:12 pages:0 https://doi.org/10.1016/j.knosys.2022.110006 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OPC-GGO 38.00 Geowissenschaften: Allgemeines VZ AR 258 2022 22 1222 0 |
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Enthalten in Subsurface fluid flow at an active cold seep area in the Qiongdongnan Basin, northern South China Sea Amsterdam [u.a.] volume:258 year:2022 day:22 month:12 pages:0 |
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Generally, existing studies focused on either using as many factors as possible or improving the model structure for describing the spatio-temporal dependence of data. However, due to the complexity and diversity of these influencing factors, few of them can successfully capture the spatial correlation and temporal dependence at the same time. Moreover, none of them have considered the high-order spatio-temporal dependence of traffic data. In fact, mathematically the development of a state can be well described by its current value, together with the series of its high order changing rate. For this insight, in this paper we propose a CNN-based architecture, called Attentive Differential Convolutional Neural Network (ADCNN), to encode the current state and its high order changing rate using the historical traffic data. Inspired by the idea of that a function can be approximated by using a finite number of terms of its Taylor expansion, we design a cascade architecture based on the Hierarchical Differential Unit (HDU) and Cross-Attention mechanism which can learn the high order changing rate of the current state. We conduct extensive experiments to evaluate ADCNN on three real-world traffic datasets. Experimental results show that ADCNN outperforms the other state-of-the-art models.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Traffic crowd flow prediction has drawn more attention in both academic and industry communities due to the explosive growth of traffic data. Generally, existing studies focused on either using as many factors as possible or improving the model structure for describing the spatio-temporal dependence of data. However, due to the complexity and diversity of these influencing factors, few of them can successfully capture the spatial correlation and temporal dependence at the same time. Moreover, none of them have considered the high-order spatio-temporal dependence of traffic data. In fact, mathematically the development of a state can be well described by its current value, together with the series of its high order changing rate. For this insight, in this paper we propose a CNN-based architecture, called Attentive Differential Convolutional Neural Network (ADCNN), to encode the current state and its high order changing rate using the historical traffic data. Inspired by the idea of that a function can be approximated by using a finite number of terms of its Taylor expansion, we design a cascade architecture based on the Hierarchical Differential Unit (HDU) and Cross-Attention mechanism which can learn the high order changing rate of the current state. 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Attentive differential convolutional neural networks for crowd flow prediction |
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Traffic crowd flow prediction has drawn more attention in both academic and industry communities due to the explosive growth of traffic data. Generally, existing studies focused on either using as many factors as possible or improving the model structure for describing the spatio-temporal dependence of data. However, due to the complexity and diversity of these influencing factors, few of them can successfully capture the spatial correlation and temporal dependence at the same time. Moreover, none of them have considered the high-order spatio-temporal dependence of traffic data. In fact, mathematically the development of a state can be well described by its current value, together with the series of its high order changing rate. For this insight, in this paper we propose a CNN-based architecture, called Attentive Differential Convolutional Neural Network (ADCNN), to encode the current state and its high order changing rate using the historical traffic data. Inspired by the idea of that a function can be approximated by using a finite number of terms of its Taylor expansion, we design a cascade architecture based on the Hierarchical Differential Unit (HDU) and Cross-Attention mechanism which can learn the high order changing rate of the current state. We conduct extensive experiments to evaluate ADCNN on three real-world traffic datasets. Experimental results show that ADCNN outperforms the other state-of-the-art models. |
| abstractGer |
Traffic crowd flow prediction has drawn more attention in both academic and industry communities due to the explosive growth of traffic data. Generally, existing studies focused on either using as many factors as possible or improving the model structure for describing the spatio-temporal dependence of data. However, due to the complexity and diversity of these influencing factors, few of them can successfully capture the spatial correlation and temporal dependence at the same time. Moreover, none of them have considered the high-order spatio-temporal dependence of traffic data. In fact, mathematically the development of a state can be well described by its current value, together with the series of its high order changing rate. For this insight, in this paper we propose a CNN-based architecture, called Attentive Differential Convolutional Neural Network (ADCNN), to encode the current state and its high order changing rate using the historical traffic data. Inspired by the idea of that a function can be approximated by using a finite number of terms of its Taylor expansion, we design a cascade architecture based on the Hierarchical Differential Unit (HDU) and Cross-Attention mechanism which can learn the high order changing rate of the current state. We conduct extensive experiments to evaluate ADCNN on three real-world traffic datasets. Experimental results show that ADCNN outperforms the other state-of-the-art models. |
| abstract_unstemmed |
Traffic crowd flow prediction has drawn more attention in both academic and industry communities due to the explosive growth of traffic data. Generally, existing studies focused on either using as many factors as possible or improving the model structure for describing the spatio-temporal dependence of data. However, due to the complexity and diversity of these influencing factors, few of them can successfully capture the spatial correlation and temporal dependence at the same time. Moreover, none of them have considered the high-order spatio-temporal dependence of traffic data. In fact, mathematically the development of a state can be well described by its current value, together with the series of its high order changing rate. For this insight, in this paper we propose a CNN-based architecture, called Attentive Differential Convolutional Neural Network (ADCNN), to encode the current state and its high order changing rate using the historical traffic data. Inspired by the idea of that a function can be approximated by using a finite number of terms of its Taylor expansion, we design a cascade architecture based on the Hierarchical Differential Unit (HDU) and Cross-Attention mechanism which can learn the high order changing rate of the current state. We conduct extensive experiments to evaluate ADCNN on three real-world traffic datasets. Experimental results show that ADCNN outperforms the other state-of-the-art models. |
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Attentive differential convolutional neural networks for crowd flow prediction |
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