Short-Term Traffic Speed Prediction Method for Urban Road Sections Based on Wavelet Transform and Gated Recurrent Unit
As a core component of the urban intelligent transportation system, traffic prediction is significant for urban traffic control and guidance. However, it is challenging to achieve accurate traffic prediction due to the complex spatiotemporal correlation of traffic data. A road section speed predicti...
Gespeichert in:
| Veröffentlicht in: | Mathematical problems in engineering 2020, Vol.2020 (2020), p.1-13 |
|---|---|
| Hauptverfasser: | , , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 13 |
|---|---|
| container_issue | 2020 |
| container_start_page | 1 |
| container_title | Mathematical problems in engineering |
| container_volume | 2020 |
| creator | Zhao, Xiaoxuan Xu, Chengyao Luo, Wei Fu, Xin |
| description | As a core component of the urban intelligent transportation system, traffic prediction is significant for urban traffic control and guidance. However, it is challenging to achieve accurate traffic prediction due to the complex spatiotemporal correlation of traffic data. A road section speed prediction model based on wavelet transform and neural network is, therefore, proposed in this article to improve traffic prediction methods. The wavelet transform is used to decompose the original traffic speed data, and then the coefficients obtained after the decomposition are used to reconstruct the high-frequency random sequences and the low-frequency trend sequence. Secondly, a GRU neural network is constructed to learn the trend of low-frequency sequence. The spatiotemporal correlation between input data is extracted by adjusting the input of the model. Meanwhile, an ARMA model is used to fit unstable random fluctuations of high-frequency sequences. Last of all, the prediction results of the two models are added together to obtain the final prediction result. The proposed prediction model is validated by using road section speed data based on the floating car data collected in Ningbo. The results show that the proposed model has high accuracy and robustness. |
| doi_str_mv | 10.1155/2020/3697625 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_journals_2407983594</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2407983594</sourcerecordid><originalsourceid>FETCH-LOGICAL-c360t-3ff367ec4a0cc1360b5d820cf28cf9a1ac8602d7ace417790b365e7f0a77c3a33</originalsourceid><addsrcrecordid>eNqF0E1LAzEQBuBFFKzVm2cJeNS1-dhsdo9atAoVpR_obZkmE7ql3a1JWvHfm1rBo6cZZh5m4E2Sc0ZvGJOyxymnPZGXKufyIOkwmYtUskwdxp7yLGVcvB8nJ94vKOVMsqKTbMfz1oV0gm5FJg6srTUZrxENeXVoah3qtiHPGOatIbZ1ZOpm0JBRC4aM8WfryR346KN7gy0uMewONT7qFYHGkAGEuB6h3jiHTSDTpg6nyZGFpcez39pNpg_3k_5jOnwZPPVvh6kWOQ2psFbkCnUGVGsWRzNpCk615YW2JTDQRU65UaAxY0qVdCZyicpSUEoLEKKbXO7vrl37sUEfqkW7cU18WfGMqrIQssyiut4r7VrvHdpq7eoVuK-K0WqXbLVLtvpNNvKrPZ_XjYHP-j99sdcYDVr406zMZEnFNxoegls</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2407983594</pqid></control><display><type>article</type><title>Short-Term Traffic Speed Prediction Method for Urban Road Sections Based on Wavelet Transform and Gated Recurrent Unit</title><source>Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals</source><source>Wiley Online Library Open Access</source><source>Alma/SFX Local Collection</source><creator>Zhao, Xiaoxuan ; Xu, Chengyao ; Luo, Wei ; Fu, Xin</creator><contributor>Hwang, Feng-Jang ; Feng-Jang Hwang</contributor><creatorcontrib>Zhao, Xiaoxuan ; Xu, Chengyao ; Luo, Wei ; Fu, Xin ; Hwang, Feng-Jang ; Feng-Jang Hwang</creatorcontrib><description>As a core component of the urban intelligent transportation system, traffic prediction is significant for urban traffic control and guidance. However, it is challenging to achieve accurate traffic prediction due to the complex spatiotemporal correlation of traffic data. A road section speed prediction model based on wavelet transform and neural network is, therefore, proposed in this article to improve traffic prediction methods. The wavelet transform is used to decompose the original traffic speed data, and then the coefficients obtained after the decomposition are used to reconstruct the high-frequency random sequences and the low-frequency trend sequence. Secondly, a GRU neural network is constructed to learn the trend of low-frequency sequence. The spatiotemporal correlation between input data is extracted by adjusting the input of the model. Meanwhile, an ARMA model is used to fit unstable random fluctuations of high-frequency sequences. Last of all, the prediction results of the two models are added together to obtain the final prediction result. The proposed prediction model is validated by using road section speed data based on the floating car data collected in Ningbo. The results show that the proposed model has high accuracy and robustness.</description><identifier>ISSN: 1024-123X</identifier><identifier>EISSN: 1563-5147</identifier><identifier>DOI: 10.1155/2020/3697625</identifier><language>eng</language><publisher>Cairo, Egypt: Hindawi Publishing Corporation</publisher><subject>Accuracy ; Algorithms ; Artificial intelligence ; Decomposition ; Engineering ; Intelligent transportation systems ; Methods ; Model accuracy ; Neural networks ; Prediction models ; Researchers ; Roads & highways ; Short term ; Speed limits ; Time series ; Traffic congestion ; Traffic control ; Traffic engineering ; Traffic flow ; Traffic information ; Traffic speed ; Transportation networks ; Transportation planning ; Wavelet transforms</subject><ispartof>Mathematical problems in engineering, 2020, Vol.2020 (2020), p.1-13</ispartof><rights>Copyright © 2020 Xin Fu et al.</rights><rights>Copyright © 2020 Xin Fu et al. This is an open access article distributed under the Creative Commons Attribution License (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. http://creativecommons.org/licenses/by/4.0</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c360t-3ff367ec4a0cc1360b5d820cf28cf9a1ac8602d7ace417790b365e7f0a77c3a33</citedby><cites>FETCH-LOGICAL-c360t-3ff367ec4a0cc1360b5d820cf28cf9a1ac8602d7ace417790b365e7f0a77c3a33</cites><orcidid>0000-0003-0848-1657</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,4024,27923,27924,27925</link.rule.ids></links><search><contributor>Hwang, Feng-Jang</contributor><contributor>Feng-Jang Hwang</contributor><creatorcontrib>Zhao, Xiaoxuan</creatorcontrib><creatorcontrib>Xu, Chengyao</creatorcontrib><creatorcontrib>Luo, Wei</creatorcontrib><creatorcontrib>Fu, Xin</creatorcontrib><title>Short-Term Traffic Speed Prediction Method for Urban Road Sections Based on Wavelet Transform and Gated Recurrent Unit</title><title>Mathematical problems in engineering</title><description>As a core component of the urban intelligent transportation system, traffic prediction is significant for urban traffic control and guidance. However, it is challenging to achieve accurate traffic prediction due to the complex spatiotemporal correlation of traffic data. A road section speed prediction model based on wavelet transform and neural network is, therefore, proposed in this article to improve traffic prediction methods. The wavelet transform is used to decompose the original traffic speed data, and then the coefficients obtained after the decomposition are used to reconstruct the high-frequency random sequences and the low-frequency trend sequence. Secondly, a GRU neural network is constructed to learn the trend of low-frequency sequence. The spatiotemporal correlation between input data is extracted by adjusting the input of the model. Meanwhile, an ARMA model is used to fit unstable random fluctuations of high-frequency sequences. Last of all, the prediction results of the two models are added together to obtain the final prediction result. The proposed prediction model is validated by using road section speed data based on the floating car data collected in Ningbo. The results show that the proposed model has high accuracy and robustness.</description><subject>Accuracy</subject><subject>Algorithms</subject><subject>Artificial intelligence</subject><subject>Decomposition</subject><subject>Engineering</subject><subject>Intelligent transportation systems</subject><subject>Methods</subject><subject>Model accuracy</subject><subject>Neural networks</subject><subject>Prediction models</subject><subject>Researchers</subject><subject>Roads & highways</subject><subject>Short term</subject><subject>Speed limits</subject><subject>Time series</subject><subject>Traffic congestion</subject><subject>Traffic control</subject><subject>Traffic engineering</subject><subject>Traffic flow</subject><subject>Traffic information</subject><subject>Traffic speed</subject><subject>Transportation networks</subject><subject>Transportation planning</subject><subject>Wavelet transforms</subject><issn>1024-123X</issn><issn>1563-5147</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>RHX</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNqF0E1LAzEQBuBFFKzVm2cJeNS1-dhsdo9atAoVpR_obZkmE7ql3a1JWvHfm1rBo6cZZh5m4E2Sc0ZvGJOyxymnPZGXKufyIOkwmYtUskwdxp7yLGVcvB8nJ94vKOVMsqKTbMfz1oV0gm5FJg6srTUZrxENeXVoah3qtiHPGOatIbZ1ZOpm0JBRC4aM8WfryR346KN7gy0uMewONT7qFYHGkAGEuB6h3jiHTSDTpg6nyZGFpcez39pNpg_3k_5jOnwZPPVvh6kWOQ2psFbkCnUGVGsWRzNpCk615YW2JTDQRU65UaAxY0qVdCZyicpSUEoLEKKbXO7vrl37sUEfqkW7cU18WfGMqrIQssyiut4r7VrvHdpq7eoVuK-K0WqXbLVLtvpNNvKrPZ_XjYHP-j99sdcYDVr406zMZEnFNxoegls</recordid><startdate>2020</startdate><enddate>2020</enddate><creator>Zhao, Xiaoxuan</creator><creator>Xu, Chengyao</creator><creator>Luo, Wei</creator><creator>Fu, Xin</creator><general>Hindawi Publishing Corporation</general><general>Hindawi</general><general>Hindawi Limited</general><scope>ADJCN</scope><scope>AHFXO</scope><scope>RHU</scope><scope>RHW</scope><scope>RHX</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7TB</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>CWDGH</scope><scope>DWQXO</scope><scope>FR3</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>JQ2</scope><scope>K7-</scope><scope>KR7</scope><scope>L6V</scope><scope>M7S</scope><scope>P5Z</scope><scope>P62</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope><orcidid>https://orcid.org/0000-0003-0848-1657</orcidid></search><sort><creationdate>2020</creationdate><title>Short-Term Traffic Speed Prediction Method for Urban Road Sections Based on Wavelet Transform and Gated Recurrent Unit</title><author>Zhao, Xiaoxuan ; Xu, Chengyao ; Luo, Wei ; Fu, Xin</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c360t-3ff367ec4a0cc1360b5d820cf28cf9a1ac8602d7ace417790b365e7f0a77c3a33</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Accuracy</topic><topic>Algorithms</topic><topic>Artificial intelligence</topic><topic>Decomposition</topic><topic>Engineering</topic><topic>Intelligent transportation systems</topic><topic>Methods</topic><topic>Model accuracy</topic><topic>Neural networks</topic><topic>Prediction models</topic><topic>Researchers</topic><topic>Roads & highways</topic><topic>Short term</topic><topic>Speed limits</topic><topic>Time series</topic><topic>Traffic congestion</topic><topic>Traffic control</topic><topic>Traffic engineering</topic><topic>Traffic flow</topic><topic>Traffic information</topic><topic>Traffic speed</topic><topic>Transportation networks</topic><topic>Transportation planning</topic><topic>Wavelet transforms</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Zhao, Xiaoxuan</creatorcontrib><creatorcontrib>Xu, Chengyao</creatorcontrib><creatorcontrib>Luo, Wei</creatorcontrib><creatorcontrib>Fu, Xin</creatorcontrib><collection>الدوريات العلمية والإحصائية - e-Marefa Academic and Statistical Periodicals</collection><collection>معرفة - المحتوى العربي الأكاديمي المتكامل - e-Marefa Academic Complete</collection><collection>Hindawi Publishing Complete</collection><collection>Hindawi Publishing Subscription Journals</collection><collection>Hindawi Publishing Open Access</collection><collection>CrossRef</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>Middle East & Africa Database</collection><collection>ProQuest Central Korea</collection><collection>Engineering Research Database</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Computer Science Collection</collection><collection>Computer Science Database</collection><collection>Civil Engineering Abstracts</collection><collection>ProQuest Engineering Collection</collection><collection>Engineering Database</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>Access via ProQuest (Open Access)</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>Engineering Collection</collection><jtitle>Mathematical problems in engineering</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Zhao, Xiaoxuan</au><au>Xu, Chengyao</au><au>Luo, Wei</au><au>Fu, Xin</au><au>Hwang, Feng-Jang</au><au>Feng-Jang Hwang</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Short-Term Traffic Speed Prediction Method for Urban Road Sections Based on Wavelet Transform and Gated Recurrent Unit</atitle><jtitle>Mathematical problems in engineering</jtitle><date>2020</date><risdate>2020</risdate><volume>2020</volume><issue>2020</issue><spage>1</spage><epage>13</epage><pages>1-13</pages><issn>1024-123X</issn><eissn>1563-5147</eissn><abstract>As a core component of the urban intelligent transportation system, traffic prediction is significant for urban traffic control and guidance. However, it is challenging to achieve accurate traffic prediction due to the complex spatiotemporal correlation of traffic data. A road section speed prediction model based on wavelet transform and neural network is, therefore, proposed in this article to improve traffic prediction methods. The wavelet transform is used to decompose the original traffic speed data, and then the coefficients obtained after the decomposition are used to reconstruct the high-frequency random sequences and the low-frequency trend sequence. Secondly, a GRU neural network is constructed to learn the trend of low-frequency sequence. The spatiotemporal correlation between input data is extracted by adjusting the input of the model. Meanwhile, an ARMA model is used to fit unstable random fluctuations of high-frequency sequences. Last of all, the prediction results of the two models are added together to obtain the final prediction result. The proposed prediction model is validated by using road section speed data based on the floating car data collected in Ningbo. The results show that the proposed model has high accuracy and robustness.</abstract><cop>Cairo, Egypt</cop><pub>Hindawi Publishing Corporation</pub><doi>10.1155/2020/3697625</doi><tpages>13</tpages><orcidid>https://orcid.org/0000-0003-0848-1657</orcidid><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 1024-123X |
| ispartof | Mathematical problems in engineering, 2020, Vol.2020 (2020), p.1-13 |
| issn | 1024-123X 1563-5147 |
| language | eng |
| recordid | cdi_proquest_journals_2407983594 |
| source | Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals; Wiley Online Library Open Access; Alma/SFX Local Collection |
| subjects | Accuracy Algorithms Artificial intelligence Decomposition Engineering Intelligent transportation systems Methods Model accuracy Neural networks Prediction models Researchers Roads & highways Short term Speed limits Time series Traffic congestion Traffic control Traffic engineering Traffic flow Traffic information Traffic speed Transportation networks Transportation planning Wavelet transforms |
| title | Short-Term Traffic Speed Prediction Method for Urban Road Sections Based on Wavelet Transform and Gated Recurrent Unit |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-19T22%3A13%3A55IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Short-Term%20Traffic%20Speed%20Prediction%20Method%20for%20Urban%20Road%20Sections%20Based%20on%20Wavelet%20Transform%20and%20Gated%20Recurrent%20Unit&rft.jtitle=Mathematical%20problems%20in%20engineering&rft.au=Zhao,%20Xiaoxuan&rft.date=2020&rft.volume=2020&rft.issue=2020&rft.spage=1&rft.epage=13&rft.pages=1-13&rft.issn=1024-123X&rft.eissn=1563-5147&rft_id=info:doi/10.1155/2020/3697625&rft_dat=%3Cproquest_cross%3E2407983594%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2407983594&rft_id=info:pmid/&rfr_iscdi=true |