Generative AI for realistic urban traffic scenario generation: β-GCN-VAE and β-Transformer-VAE models

Generative AI for realistic urban traffic scenario generation: β-GCN-VAE and β-Transformer-VAE models | 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 Research Article Generative AI for realistic urban traffic scenario generation: β-GCN-VAE and β-Transformer-VAE models Michele Carbonera, Michele Ciavotta, Enza Messina This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8490312/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 Generating plausible traffic‑speed scenarios over multiple interconnected road segments is crucial for applications such as traffic optimization, urban‑planning analysis, and traffic management. Traditional statistical approaches, such as Copulas, tend to be computationally expensive both during training and scenario generation, which severely limits their applicability to large urban networks. To address these challenges, two novel deep generative architectures for synthesizing realistic spatial traffic‑speed scenarios across urban road networks are proposed: a Graph-based $\beta$-Variational Autoencoder with Dual Latent Vectors ($\beta$-GCN-VAE) and a Transformer-based Variational Autoencoder ($\beta$-T-VAE). The $\beta$-GCN-VAE enhances the variational framework by adopting a higher $\beta$ coefficient to encourage more disentangled latent representations, while the $\beta$‑T‑VAE incorporates a transformer decoder with multi‑head non-causal self‑attention, enabling direct modeling of long‑range spatial dependencies that cannot be captured by convolutional structures alone. The paper also compares two $\beta$‑scheduling strategies, linear and periodic, to assess how different annealing dynamics influence reconstruction quality, latent‑space organization, and overall generative performance. Both architectures are trained on large-scale urban traffic-speed datasets collected in the Chinese megacity of Chengdu. Extensive experiments show that they substantially outperform standard VAEs, GANs, and the statistical Copula baselines in terms of distributional fidelity, spatial coherence, and computational efficiency. UrbanMobility VAE Transportation Generative 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-8490312","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":569247788,"identity":"40ff8d97-7ff3-466a-a3f7-302c2ed05f2e","order_by":0,"name":"Michele 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