Multi-view Traffic Flow Prediction Model Based on Spatiotemporal Convolution | Journal of Computational and Cognitive Engineering

<!DOCTYPE html> <html lang="en" xml:lang="en"> <head> <meta charset="utf-8"> <meta name="viewport" content="width=device-width, initial-scale=1.0"> <title> Multi-view Traffic Flow Prediction Model Based on Spatiotemporal Convolution | Journal of Computational and Cognitive Engineering </title> <link rel="icon" href="https://ojs.bonviewpress.com/public/journals/4/favicon_en_US.png"> <meta name="generator" content="Open Journal Systems 3.4.0.7"> <meta name="gs_meta_revision" content="1.1"/> <meta name="citation_journal_title" content="Journal of Computational and Cognitive Engineering"/> <meta name="citation_journal_abbrev" content="JCCE"/> <meta name="citation_issn" content="2810-9503"/> <meta name="citation_author" content="Xiaoyan Zhu"/> <meta name="citation_author_institution" content="College of Computer Science and Technology, Qingdao University, China"/> <meta name="citation_title" content="Multi-view Traffic Flow Prediction Model Based on Spatiotemporal Convolution"/> <meta name="citation_language" content="en"/> <meta name="citation_date" content="2024/11/22"/> <meta name="citation_volume" content="3"/> <meta name="citation_issue" content="4"/> <meta name="citation_firstpage" content="395"/> <meta name="citation_lastpage" content="403"/> <meta name="citation_doi" content="10.47852/bonviewJCCE42022914"/> <meta name="citation_abstract_html_url" content="https://ojs.bonviewpress.com/index.php/JCCE/article/view/2914"/> <meta name="citation_abstract" xml:lang="en" content="Predicting traffic flow has always been a significant task in intelligent transportation systems. Due to the substantial temporal and spatial dependencies of traffic flow sequences, accurately predicting traffic flow poses a considerable challenge. Many existing works primarily rely on recurrent neural networks, graph neural networks, and Transformer models to establish traffic flow prediction models. To better extract features and enhance efficiency, a traffic flow prediction model based on multi-view spatiotemporal convolution (MVSC) is proposed. This model learns the representation of sequence data at the input encoding layer and incorporates location and time information. In the spatiotemporal feature representation learning layer, considering the diverse periodic patterns in sequences, several representation learning modules are designed, conducting local spatiotemporal feature exploration through one-dimensional convolution and then accomplishing global spatiotemporal feature mining based on causal convolution. To further enhance the model's utilization of spatiotemporal features, a channel attention mechanism is introduced at the prediction layer. The forecasting method employed in the study is direct multistep, and subsequent experiments conducted on two real datasets demonstrate that the MVSC model exhibits a certain degree of superiority in MAE, RMSE, and MAPE for both short-term and long-term predictions compared to existing models. And through the latest experiments and investigations, it has been found that MVSC has improved MAPE performance by about 1.2% compared to recent models such as RTGCN and STRGCN, achieving the intended outcomes. Received: 24 March 2024 | Revised: 13 June 2024 | Accepted: 17 July 2024 Conflicts of Interest The author declares that she has no conflicts of interest to this work. Data Availability Statement Data sharing is not applicable to this article as no new data were created or analyzed in this study. Author Contribution Statement Xiaoyan Zhu: Conceptualization, Methodology, Software, Validation, Investigation, Data curation, Writing – original draft, Writing – review &amp; editing, Visualization, Supervision, Project administration."/> <meta name="citation_keywords" xml:lang="en" content="traffic engineering"/> <meta name="citation_keywords" xml:lang="en" content="traffic flow prediction"/> <meta name="citation_keywords" xml:lang="en" content="spatiotemporal convolution"/> <meta name="citation_keywords" xml:lang="en" content=" attention mechanism"/> <meta name="citation_keywords" xml:lang="en" content="Transformer"/> <meta name="citation_pdf_url" content="https://ojs.bonviewpress.com/index.php/JCCE/article/download/2914/1073"/> <link rel="schema.DC" href="http://purl.org/dc/elements/1.1/" /> <meta name="DC.Creator.PersonalName" content="Xiaoyan Zhu"/> <meta name="DC.Date.created" scheme="ISO8601" content="2024-11-22"/> <meta name="DC.Date.dateSubmitted" scheme="ISO8601" content="2024-03-24"/> <meta name="DC.Date.issued" scheme="ISO8601" content="2024-11-22"/> <meta name="DC.Date.modified" scheme="ISO8601" content="2024-11-22"/> <meta name="DC.Description" xml:lang="en" content="Predicting traffic flow has always been a significant task in intelligent transportation systems. Due to the substantial temporal and spatial dependencies of traffic flow sequences, accurately predicting traffic flow poses a considerable challenge. Many existing works primarily rely on recurrent neural networks, graph neural networks, and Transformer models to establish traffic flow prediction models. To better extract features and enhance efficiency, a traffic flow prediction model based on multi-view spatiotemporal convolution (MVSC) is proposed. This model learns the representation of sequence data at the input encoding layer and incorporates location and time information. In the spatiotemporal feature representation learning layer, considering the diverse periodic patterns in sequences, several representation learning modules are designed, conducting local spatiotemporal feature exploration through one-dimensional convolution and then accomplishing global spatiotemporal feature mining based on causal convolution. To further enhance the model's utilization of spatiotemporal features, a channel attention mechanism is introduced at the prediction layer. The forecasting method employed in the study is direct multistep, and subsequent experiments conducted on two real datasets demonstrate that the MVSC model exhibits a certain degree of superiority in MAE, RMSE, and MAPE for both short-term and long-term predictions compared to existing models. And through the latest experiments and investigations, it has been found that MVSC has improved MAPE performance by about 1.2% compared to recent models such as RTGCN and STRGCN, achieving the intended outcomes. Received: 24 March 2024 | Revised: 13 June 2024 | Accepted: 17 July 2024 Conflicts of Interest The author declares that she has no conflicts of interest to this work. Data Availability Statement Data sharing is not applicable to this article as no new data were created or analyzed in this study. Author Contribution Statement Xiaoyan Zhu: Conceptualization, Methodology, Software, Validation, Investigation, Data curation, Writing – original draft, Writing – review &amp; editing, Visualization, Supervision, Project administration."/> <meta name="DC.Format" scheme="IMT" content="application/pdf"/> <meta name="DC.Identifier" content="2914"/> <meta name="DC.Identifier.pageNumber" content="395-403"/> <meta name="DC.Identifier.DOI" content="10.47852/bonviewJCCE42022914"/> <meta name="DC.Identifier.URI" content="https://ojs.bonviewpress.com/index.php/JCCE/article/view/2914"/> <meta name="DC.Language" scheme="ISO639-1" content="en"/> <meta name="DC.Rights" content="Copyright (c) 2024 Author"/> <meta name="DC.Rights" content="https://creativecommons.org/licenses/by/4.0/"/> <meta name="DC.Source" content="Journal of Computational and Cognitive Engineering"/> <meta name="DC.Source.ISSN" content="2810-9503"/> <meta name="DC.Source.Issue" content="4"/> <meta name="DC.Source.Volume" 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class="authors"> <li> <span class="name"> Xiaoyan Zhu </span> <span class="affiliation"> College of Computer Science and Technology, Qingdao University, China </span> <span class="orcid"> <svg class="orcid_icon" viewBox="0 0 256 256" aria-hidden="true"> <style type="text/css"> .st0{fill:#A6CE39;} .st1{fill:#FFFFFF;} </style> <path class="st0" d="M256,128c0,70.7-57.3,128-128,128C57.3,256,0,198.7,0,128C0,57.3,57.3,0,128,0C198.7,0,256,57.3,256,128z"/> <g> <path class="st1" d="M86.3,186.2H70.9V79.1h15.4v48.4V186.2z"/> <path class="st1" d="M108.9,79.1h41.6c39.6,0,57,28.3,57,53.6c0,27.5-21.5,53.6-56.8,53.6h-41.8V79.1z M124.3,172.4h24.5 c34.9,0,42.9-26.5,42.9-39.7c0-21.5-13.7-39.7-43.7-39.7h-23.7V172.4z"/> <path class="st1" d="M88.7,56.8c0,5.5-4.5,10.1-10.1,10.1c-5.6,0-10.1-4.6-10.1-10.1c0-5.6,4.5-10.1,10.1-10.1 C84.2,46.7,88.7,51.3,88.7,56.8z"/> </g> </svg> <a href="https://orcid.org/0009-0003-1794-6820" target="_blank"> https://orcid.org/0009-0003-1794-6820 </a> </span> </li> </ul> </section> <section class="item doi"> <h2 class="label"> DOI: </h2> <span class="value"> <a href="https://doi.org/10.47852/bonviewJCCE42022914"> https://doi.org/10.47852/bonviewJCCE42022914 </a> </span> </section> <section class="item keywords"> <h2 class="label"> Keywords: </h2> <span class="value"> traffic engineering, traffic flow prediction, spatiotemporal convolution, attention mechanism, Transformer </span> </section> <section class="item abstract"> <h2 class="label">Abstract</h2> <p>Predicting traffic flow has always been a significant task in intelligent transportation systems. Due to the substantial temporal and spatial dependencies of traffic flow sequences, accurately predicting traffic flow poses a considerable challenge. Many existing works primarily rely on recurrent neural networks, graph neural networks, and Transformer models to establish traffic flow prediction models. To better extract features and enhance efficiency, a traffic flow prediction model based on multi-view spatiotemporal convolution (MVSC) is proposed. This model learns the representation of sequence data at the input encoding layer and incorporates location and time information. In the spatiotemporal feature representation learning layer, considering the diverse periodic patterns in sequences, several representation learning modules are designed, conducting local spatiotemporal feature exploration through one-dimensional convolution and then accomplishing global spatiotemporal feature mining based on causal convolution. To further enhance the model's utilization of spatiotemporal features, a channel attention mechanism is introduced at the prediction layer. The forecasting method employed in the study is direct multistep, and subsequent experiments conducted on two real datasets demonstrate that the MVSC model exhibits a certain degree of superiority in MAE, RMSE, and MAPE for both short-term and long-term predictions compared to existing models. And through the latest experiments and investigations, it has been found that MVSC has improved MAPE performance by about 1.2% compared to recent models such as RTGCN and STRGCN, achieving the intended outcomes.</p> <p> </p> <p><strong>Received</strong>: 24 March 2024 | <strong>Revised</strong>: 13 June 2024 | <strong>Accepted</strong>: 17 July 2024</p> <p> </p> <p><strong>Conflicts of Interest</strong></p> <p>The author declares that she has no conflicts of interest to this work.</p> <p> </p> <p><strong>Data Availability Statement</strong></p> <p>Data sharing is not applicable to this article as no new data were created or analyzed in this study.</p> <p> </p> <p><strong>Author Contribution Statement</strong></p> <p><strong>Xiaoyan Zhu: </strong>Conceptualization, Methodology, Software, Validation, Investigation, Data curation, Writing – original draft, Writing – review & editing, Visualization, Supervision, Project administration.</p> </section> <br /><div class="separator"></div><div class="item abstract" id="trendmd-suggestions"></div><script defer src='//js.trendmd.com/trendmd.min.js' data-trendmdconfig='{"website_id":"89267", "element":"#trendmd-suggestions"}'></script><div class="item downloads_chart"> <h3 class="label"> Metrics </h3> <div id="paperbuzz"><div id="loading">Metrics Loading ...</div></div> <script type="text/javascript"> window.onload = function () { var options = { paperbuzzStatsJson: JSON.parse('{\"altmetrics_sources\":[{\"events\":null,\"events_count\":2,\"events_count_by_day\":[{\"count\":\"2\",\"date\":\"2024-11-22\"}],\"events_count_by_month\":[{\"count\":\"2\",\"date\":\"2024-11\"}],\"events_count_by_year\":[{\"count\":\"2\",\"date\":\"2024\"}],\"source\":{\"display_name\":\"File downloads\"},\"source_id\":\"fileDownloads\"}],\"crossref_event_data_url\":\"https:\\/\\/api.eventdata.crossref.org\\/v1\\/events?rows=1000&filter=from-collected-date:1990-01-01,until-collected-date:2099-01-01,obj-id:10.47852\\/bonviewjcce42022914\",\"doi\":\"10.47852\\/bonviewjcce42022914\",\"metadata\":{\"DOI\":\"10.47852\\/bonviewjcce42022914\",\"ISSN\":[\"2810-9503\"],\"URL\":\"http:\\/\\/dx.doi.org\\/10.47852\\/bonviewjcce42022914\",\"abstract\":\"<jats:p>Predicting traffic flow has always been a significant task in intelligent transportation systems. 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