Designing Machine Learning Driven Dual Adjacency Graph Based Spatiotemporal Traffic Prediction for Smarter Urban Mobility and Congestion Managemnet in Bengaluru City

Designing Machine Learning Driven Dual Adjacency Graph Based Spatiotemporal Traffic Prediction for Smarter Urban Mobility and Congestion Managemnet in Bengaluru City | 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 Designing Machine Learning Driven Dual Adjacency Graph Based Spatiotemporal Traffic Prediction for Smarter Urban Mobility and Congestion Managemnet in Bengaluru City Sathish Kumar Ravichandran, Chin Shiuh Shieh, Mong Fong Horng, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7174769/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 21 Nov, 2025 Read the published version in Scientific Reports → Version 1 posted 12 You are reading this latest preprint version Abstract With the mushrooming inflation in the population, transportation systems are challenged by several issues. Traffic congestion is customary and traffic accidents occurs often repeatedly deteriorating traffic environments. To take the edge of these issues and enhance the transportation efficiency, accurate traffic forecasting is critical. Accurate temporal time-dependent traffic predictions are essential for ensuring safety and efficiency of intelligent traffic management system. Nevertheless, owing to the intrinsic spatial and temporal dependencies of traffic flow, it is still a challenging problem. To solve this, some methods are proposed taking into consideration the detailed traffic patterns across major roads and intersections, while the complicated spatiotemporal dynamics and interdependencies between traffic flows are not taken into account.In this work a method called, Gaussian Dual Adjacency Graph-based Spatial Correlated and Temporal Time-dependent (GDAG-SCTT) traffic prediction in Bangalore city is proposed. Initially with the raw traffic patterns obtained from Bangalore’s traffic pulse dataset as input are subjected to Local-Global Invariant Inter Quartile and Min-Max Normalization based Traffic Data Pre-processing. By applying this pre-processing outliers are removed and finally normalized pre-processed results are obtained. Next, extraction of spatial and temporal features is done by using Gaussian Kernel Dynamic Adjacency based Spatial Correlated and Temporal Time-dependency based feature extraction model. Here, first, Spatial Correlated Graph Convolutional Neural Network is applied to extract spatial features, following which Temporal Long Short Term Time-dependency Memory is applied to extract temporal features. To evaluate the GDAG-SCTT methods performance, five different performance metrics, precision, recall, accuracy, root mean square error and training time are validated and analyzed. The GDAG-SCTT achieved higher performance compared to other state-of-the-art methods on our collected Bangalore’s traffic pulse dataset demonstrating the efficiency in reducing root mean square error by 28% while improving overall accuracy by 25% in an extensive manner. Physical sciences/Engineering Physical sciences/Mathematics and computing Local-Global Invariant Inter Quartile Min-Max Normalization Spatial Correlated Graph Convolutional Neural Network Temporal Long Short Term Time-dependency Memory Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 21 Nov, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 18 Sep, 2025 Reviews received at journal 12 Sep, 2025 Reviewers agreed at journal 06 Sep, 2025 Reviewers agreed at journal 22 Aug, 2025 Reviewers agreed at journal 22 Aug, 2025 Reviews received at journal 22 Aug, 2025 Reviewers agreed at journal 22 Aug, 2025 Reviewers invited by journal 18 Aug, 2025 Editor assigned by journal 18 Aug, 2025 Editor invited by journal 08 Aug, 2025 Submission checks completed at journal 07 Aug, 2025 First submitted to journal 07 Aug, 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-7174769","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":504932121,"identity":"cbba0aa0-58bb-4070-a51a-f4528bc77c51","order_by":0,"name":"Sathish Kumar Ravichandran","email":"data:image/png;base64,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","orcid":"","institution":"National Kaohsiung University of Science and Technology","correspondingAuthor":true,"prefix":"","firstName":"Sathish","middleName":"Kumar","lastName":"Ravichandran","suffix":""},{"id":504932123,"identity":"e5ec4557-8046-4eeb-8671-99ad7982e162","order_by":1,"name":"Chin Shiuh Shieh","email":"","orcid":"","institution":"National Kaohsiung University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Chin","middleName":"Shiuh","lastName":"Shieh","suffix":""},{"id":504932124,"identity":"4b96630d-ddd6-488b-a702-03276468b4ce","order_by":2,"name":"Mong Fong Horng","email":"","orcid":"","institution":"National Kaohsiung University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Mong","middleName":"Fong","lastName":"Horng","suffix":""},{"id":504932125,"identity":"297ec65a-333e-43f5-a4b7-37dcd4b5a427","order_by":3,"name":"Arulmurugan Ramu","email":"","orcid":"","institution":"Aktobe Regional State University named after K. 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