Leveraging Mobility Data to Simulate Trip Chains in Traffic Systems Using a k-anonymised Bayesian Network

Leveraging Mobility Data to Simulate Trip Chains in Traffic Systems Using a k-anonymised Bayesian 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 Research Article Leveraging Mobility Data to Simulate Trip Chains in Traffic Systems Using a k-anonymised Bayesian Network Gian Truöl, Damir Ravlija, Clemens Krüger, Matvii Shevchenko, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8649175/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 transportation sector plays a crucial role in reducing global greenhouse gas emissions. In this context, simulation models enable virtual testing of intelligent mobility solutions and prediction of mobility behaviour under various conditions. Improving the accuracy of these models requires insights from real-world data. This need often conflicts with anonymisation and data protection requirements, particularly for small data sets. Privacy-preserving methods are therefore essential to safeguard data donors’ privacy while leveraging real mobility data to enhance simulation models.This work introduces a method for extracting information about individual mobility behaviour that ensures data privacy and integrates the data into a detailed traffic simulation model. A Bayesian network is constructed to capture dependencies between trip variables such as trip purpose and start time. The network graph is learned using a genetic algorithm applied to k-anonymised data. Conditional distributions derived from these dependencies are then used to generate synthetic, individual trip chains that serve as input for a traffic simulation in the open-source software SUMO. As a result, synthetic trajectory data are produced, enabling the study of different mobility behaviours. The two main contributions of this work are the method to build an anonymised detailed trip chain model and the usage of this model as an input for a traffic simulation model.The proposed method has been validated and tested with real mobility data and can be extended in the future by incorporating additional data sources and assumptions to further improve simulation accuracy. Trip chain Bayesian network SUMO K-anonymity Activity-based Mobility behaviour Simulation Full Text Additional Declarations No competing interests reported. Supplementary Files tripchainbasedsumosimulationmain3.zip 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-8649175","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":592793500,"identity":"e73f47ee-e36d-4bc3-a9a8-25746d519026","order_by":0,"name":"Gian 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