{"paper_id":"1064af5d-3fb5-4f9f-9611-7eb6939c19c0","body_text":"Predicting hourly traffic volume of urban signal intersections using Dynamically Weighted LightGBM | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Predicting hourly traffic volume of urban signal intersections using Dynamically Weighted LightGBM Wang Bozhi, Steve SHYH-Ching Chen, Zhou Yue This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7976576/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 10 Apr, 2026 Read the published version in Scientific Reports → Version 1 posted 9 You are reading this latest preprint version Abstract Predicting traffic volume of urban intersections is critical to ensure the stability and efficiency of urban road network. To better capture spatio-temporal nonlinear correlation between traffic volume and contributing factors and improve the prediction performance, this study introduces a Dynamically Weighted LightGBM (Gradient Boosting Machine) framework (DW-LGBM) to forcast the hourly intersection traffic volume. The improvement of the DW-LGBM includes three core components: a dynamic weight allocation component that captures nonlinear spatio-temporal dependency, a multi-dimensional feature engineering component that incorporates cyclical temporal trend, and a dual-stage noise suppression mechanism using Exponentially Weighted Moving Average (EWMA) and Kalman filtering to smooth the data. The proposed model is trained and tested with hourly traffic volume collected from 209 urban intersections during 31 days in Chengdu China. The results show that the predictions achieve superior performance metrics which surpass those of the baseline models (e.g., LSTM and XGBoost). The proposed architecture exhibits exceptional spatio-temporal adaptability for different urban intersections. However, it is found that all the models perform woeful in predicting traffic volume during peak hours due to the significant heterogeneity among intersections. Physical sciences/Engineering Physical sciences/Mathematics and computing intersection hourly traffic volume forecast dynamic weighting LightGBM Full Text Additional Declarations No competing interests reported. Supplementary Files file.xlsx Cite Share Download PDF Status: Published Journal Publication published 10 Apr, 2026 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 23 Feb, 2026 Reviews received at journal 16 Feb, 2026 Reviewers agreed at journal 27 Jan, 2026 Reviews received at journal 19 Jan, 2026 Reviewers agreed at journal 12 Jan, 2026 Reviewers invited by journal 12 Jan, 2026 Editor assigned by journal 11 Nov, 2025 Submission checks completed at journal 04 Nov, 2025 First submitted to journal 04 Nov, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {\"props\":{\"pageProps\":{\"initialData\":{\"identity\":\"rs-7976576\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":false,\"archivedVersions\":[],\"articleType\":\"Article\",\"associatedPublications\":[],\"authors\":[{\"id\":573564870,\"identity\":\"2cfd3c25-aa83-466f-9725-454ce9a4ae27\",\"order_by\":0,\"name\":\"Wang Bozhi\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Southwest Jiaotong Univesity\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Wang\",\"middleName\":\"\",\"lastName\":\"Bozhi\",\"suffix\":\"\"},{\"id\":573564872,\"identity\":\"6535d75e-7126-4e89-83fb-65ef19156235\",\"order_by\":1,\"name\":\"Steve SHYH-Ching 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Reports\",\"twitterHandle\":\"\",\"acdcEnabled\":true,\"dfaEnabled\":true,\"editorialSystem\":\"stoa\",\"reportingPortfolio\":\"Scientific Reports\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":true},\"keywords\":\"intersection, hourly traffic volume forecast, dynamic weighting LightGBM\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-7976576/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-7976576/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003ePredicting traffic volume of urban intersections is critical to ensure the stability and efficiency of urban road network. To better capture spatio-temporal nonlinear correlation between traffic volume and contributing factors and improve the prediction performance, this study introduces a Dynamically Weighted LightGBM (Gradient Boosting Machine) framework (DW-LGBM) to forcast the hourly intersection traffic volume. The improvement of the DW-LGBM includes three core components: a dynamic weight allocation component that captures nonlinear spatio-temporal dependency, a multi-dimensional feature engineering component that incorporates cyclical temporal trend, and a dual-stage noise suppression mechanism using Exponentially Weighted Moving Average (EWMA) and Kalman filtering to smooth the data. The proposed model is trained and tested with hourly traffic volume collected from 209 urban intersections during 31 days in Chengdu China. The results show that the predictions achieve superior performance metrics which surpass those of the baseline models (e.g., LSTM and XGBoost). The proposed architecture exhibits exceptional spatio-temporal adaptability for different urban intersections. 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