A generative adversarial network algorithm for inferring age attributes in subway based on AFC and POI data

A generative adversarial network algorithm for inferring age attributes in subway based on AFC and POI data | 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 A generative adversarial network algorithm for inferring age attributes in subway based on AFC and POI data Xinyue Xu, Anzhong Zhang, Jun Liu, Yuankai Wu, Linqiao Qin, Ziyang Ye, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4566800/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 Understanding the socioeconomic characteristics of passengers is crucial for intelligent services for subway systems. Nevertheless, obtaining such information poses challenges, especially sensitive attributes such as age, due to heightened considerations for data privacy. To address this challenge, we explore the correlation between travel features and age from both macro and micro perspectives and propose an improved Generative Adversarial Network (GAN) for age inference in subway contexts. In the macro level, we extract statistical features from Automatic Fare Collection data and novel features from Points of Interest data. The novel features allow us to examine the relationship between built environment and age. In the micro level, we introduce a comprehensive travel chain, considering the dynamic evolution of passenger trips during multiple days. To address the sample imbalance across age groups, we propose an improved GAN-based framework to infer the age attribute. Our framework incorporates Inner Product to capture interdependency among macro features and employs a Transformer-based module to extract sequential and spatiotemporal features within micro-level travel chains. Finally, the framework is validated using a case study of Guangzhou subway. The results indicate the superiority of our model over baseline approaches like Random Forest, Bayesian Networks, and Variational Autoencoders, showcasing accuracy improvements of 17.75%, 20.29%, and 16.10%, respectively. The use of Inner Product and Transformer-based modules enhances the predictive accuracy of the model. The utilization of travel chains proves to be effective in capturing travel features and improves the model’s accuracy in age inference. Furthermore, we delve into potential applications. Age inference macro features micro features trip chains generative adversarial networks 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-4566800","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":313548413,"identity":"bda6450b-4e45-4530-9702-4e741022af4b","order_by":0,"name":"Xinyue 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