{"ID":2830939,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2602.19404","arxiv_id":"2602.19404","title":"One Size Fits None: Modeling NYC Taxi Trips","abstract":"The rise of app-based ride-sharing has fundamentally changed tipping culture in New York City. We analyzed 280 million trips from 2024 to see if we could predict tips for traditional taxis versus high-volume for-hire services. By testing methods from linear regression to deep neural networks, we found two very different outcomes. Traditional taxis are highly predictable ($R^2 \\approx 0.72$) due to the in-car payment screen. In contrast, app-based tipping is random and hard to model ($R^2 \\approx 0.17$). In conclusion, we show that building one universal model is a mistake and, due to Simpson's paradox, a combined model looks accurate on average but fails to predict tips for individual taxi categories requiring specialized models.","short_abstract":"The rise of app-based ride-sharing has fundamentally changed tipping culture in New York City. We analyzed 280 million trips from 2024 to see if we could predict tips for traditional taxis versus high-volume for-hire services. By testing methods from linear regression to deep neural networks, we found two very differen...","url_abs":"https://arxiv.org/abs/2602.19404","url_pdf":"https://arxiv.org/pdf/2602.19404v1","authors":"[\"Tomas Eglinskas\"]","published":"2025-12-10T23:20:40Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"cs.AI\"]","methods":"[]","has_code":false}