Interdependencies between electricity consumption and transit and their potential to forecast transit demand in urban settings

Interdependencies between electricity consumption and transit and their potential to forecast transit demand in urban settings | 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 Interdependencies between electricity consumption and transit and their potential to forecast transit demand in urban settings Juan Acosta-Sequeda, Ömer Verbas, Joshua Auld, Sybil Derrible This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6959831/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 6 You are reading this latest preprint version Abstract This study explores the interdependencies between electricity consumption and public transit demand to forecast transportation demand in urban settings. The premise is that changes in residential electricity usage reflect human activity that can be used to predict local transportation needs. By leveraging high-resolution electricity usage data from ComEd (Chicago’s electricity provider) and transit ridership data from the Chicago Transit Authority (CTA), the study evaluates whether transit boardings can be predicted based on electricity usage changes. Three forecasting models are tested: Kalman Filter Forecasting, Long Short Term Memory Network, and Light Gradient Boosting Machine. The results indicate that LightGBM performs best, effectively capturing workday and weekend patterns. Including electricity usage as a covariate generally improves forecasting accuracy, although the benefits vary by model type. Moreover, we showcase the suitability of this approach when urban transit demand suffers from system shocks due to unforeseen phenomena such as the COVID-19 pandemic. The study concludes that electricity usage data can be a reliable predictor of transportation demand, particularly in high-activity zones. It highlights the potential for enhancing accuracy by integrating other transportation modes and non-residential electricity data. Interrelated Urban Systems Travel Demand Electricity Demand Time Series Forecasting Machine Learning Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 09 Aug, 2025 Reviewers agreed at journal 09 Aug, 2025 Reviewers invited by journal 09 Aug, 2025 Editor assigned by journal 07 Aug, 2025 Submission checks completed at journal 27 Jun, 2025 First submitted to journal 23 Jun, 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. 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The premise is that changes in residential electricity usage reflect human activity that can be used to predict local transportation needs. By leveraging high-resolution electricity usage data from ComEd (Chicago\u0026rsquo;s electricity provider) and transit ridership data from the Chicago Transit Authority (CTA), the study evaluates whether transit boardings can be predicted based on electricity usage changes. Three forecasting models are tested: Kalman Filter Forecasting, Long Short Term Memory Network, and Light Gradient Boosting Machine. The results indicate that LightGBM performs best, effectively capturing workday and weekend patterns. Including electricity usage as a covariate generally improves forecasting accuracy, although the benefits vary by model type. Moreover, we showcase the suitability of this approach when urban transit demand suffers from system shocks due to unforeseen phenomena such as the COVID-19 pandemic. 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