Traffic Congestion Prediction using Queuing Theory, Decision Tree, Random Forest, and Deep Belief Network

Traffic Congestion Prediction using Queuing Theory, Decision Tree, Random Forest, and Deep Belief Network | 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 Traffic Congestion Prediction using Queuing Theory, Decision Tree, Random Forest, and Deep Belief Network Aditi Jha, R. S. Pandey, Gyannedra Tiwary, Gaurav Vishnu Londhe This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7705031/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The exponential growth of urban populations and vehicular owner- ship has escalated the challenge of road traffic congestion in both developed and developing countries. Congestion not only increases travel delays but also aggravates fuel consumption and environmental pollution, impeding the vision of smart, sustainable cities. Traditional statistical and rule-based traffic predic- tion models often fail to capture the dynamic and nonlinear evolution of con- gestion, especially under the influence of exogenous factors such as weather, road incidents, and infrastructural changes. Recent advancements in machine learning and deep learning have offered new paradigms for data-driven, adap- tive traffic prediction. In this research, we propose a comprehensive framework that synergistically combines queuing theory with machine learning algo- rithms—specifically Decision Tree, Random Forest, and Deep Belief Network (DBN)—to provide both interpretable and robust predictions of traffic conges- tion. The model integrates historical and real-time multimodal data, including GPS trajectories, sensor feeds, and incident reports, while leveraging queuing theory to model the fundamental dynamics of vehicular flow at the micro-level. Decision Trees facilitate rule-based feature dominance and initial classification, Random Forests enhance robustness and feature selection, and DBNs perform deep spatiotemporal feature learning. Experimental validation on real-world datasets demonstrates that the proposed hybrid model outperforms conven- tional approaches on key accuracy metrics such as precision and recall. This research contributes a scalable and generalizable traffic congestion prediction solution aligned with the evolving needs of intelligent transportation systems and urban mobility management. Physical sciences/Engineering Physical sciences/Mathematics and computing Traffic Congestion Machine Learning Decision Tree Random Forest Deep Belief Network Queuing Theory Intelligent Transportation Sys- tem Traffic Prediction Data Analytics Hybrid Model Urban Mobility Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted 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. 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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-7705031","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":536959874,"identity":"bdbf9004-68cf-4ae8-a614-864fc17b88bc","order_by":0,"name":"Aditi Jha","email":"","orcid":"","institution":"Birla Institute of Technology","correspondingAuthor":false,"prefix":"","firstName":"Aditi","middleName":"","lastName":"Jha","suffix":""},{"id":536959875,"identity":"b82d26da-3512-47a5-bbbb-14543f1cbe4f","order_by":1,"name":"R. S. 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