Research on Urban Traffic Industrial Management under Big Data: Taking Traffic Congestion as an Example
This paper establishes a prediction model of traffic flow, where three cycle dependent components are used to model three characteristics of traffic data, respectively. CNN is used to extract spatial features, and the combination of LSTM and attention mechanism is used to dynamically capture the inf...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2022-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2022/1615482 |
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| _version_ | 1832554953483223040 |
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| author | Yi Zhang Shuwang Yang Hang Zhang |
| author_facet | Yi Zhang Shuwang Yang Hang Zhang |
| author_sort | Yi Zhang |
| collection | DOAJ |
| description | This paper establishes a prediction model of traffic flow, where three cycle dependent components are used to model three characteristics of traffic data, respectively. CNN is used to extract spatial features, and the combination of LSTM and attention mechanism is used to dynamically capture the influence of historical period on target period. Finally, the results are obtained by weighted integration of each component. Its prediction result is more accurate, which can provide reference for governance of urban transportation industry under the background of big data. |
| format | Article |
| id | doaj-art-1a54c035fba545799c41cc17b5873dec |
| institution | Kabale University |
| issn | 2042-3195 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-1a54c035fba545799c41cc17b5873dec2025-02-03T05:49:59ZengWileyJournal of Advanced Transportation2042-31952022-01-01202210.1155/2022/1615482Research on Urban Traffic Industrial Management under Big Data: Taking Traffic Congestion as an ExampleYi Zhang0Shuwang Yang1Hang Zhang2School of Economics and ManagementSchool of Economics and ManagementAdministration for Market Regulation of Henan ProvinceThis paper establishes a prediction model of traffic flow, where three cycle dependent components are used to model three characteristics of traffic data, respectively. CNN is used to extract spatial features, and the combination of LSTM and attention mechanism is used to dynamically capture the influence of historical period on target period. Finally, the results are obtained by weighted integration of each component. Its prediction result is more accurate, which can provide reference for governance of urban transportation industry under the background of big data.http://dx.doi.org/10.1155/2022/1615482 |
| spellingShingle | Yi Zhang Shuwang Yang Hang Zhang Research on Urban Traffic Industrial Management under Big Data: Taking Traffic Congestion as an Example Journal of Advanced Transportation |
| title | Research on Urban Traffic Industrial Management under Big Data: Taking Traffic Congestion as an Example |
| title_full | Research on Urban Traffic Industrial Management under Big Data: Taking Traffic Congestion as an Example |
| title_fullStr | Research on Urban Traffic Industrial Management under Big Data: Taking Traffic Congestion as an Example |
| title_full_unstemmed | Research on Urban Traffic Industrial Management under Big Data: Taking Traffic Congestion as an Example |
| title_short | Research on Urban Traffic Industrial Management under Big Data: Taking Traffic Congestion as an Example |
| title_sort | research on urban traffic industrial management under big data taking traffic congestion as an example |
| url | http://dx.doi.org/10.1155/2022/1615482 |
| work_keys_str_mv | AT yizhang researchonurbantrafficindustrialmanagementunderbigdatatakingtrafficcongestionasanexample AT shuwangyang researchonurbantrafficindustrialmanagementunderbigdatatakingtrafficcongestionasanexample AT hangzhang researchonurbantrafficindustrialmanagementunderbigdatatakingtrafficcongestionasanexample |