One Size Fits None: Modeling NYC Taxi Trips
Not on the map. Why?
Abstract (arXiv)
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 (R2 ≈ 0.72) due to the in-car payment screen. In contrast, app-based tipping is random and hard to model (R2 ≈ 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.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 4 of its 26 references that are in the map. First posted on arXiv 10 Dec 2025.
Builds on
4 of 26 references.
References · 4
- 2018Tweedie Gradient Boosting for Extremely Unbalanced Zero-inflated Datacited by 10
- 2018The Roots of Bias on Ubercited by 1
- 2021CatBoost model with synthetic features in application to loan risk assessment of small businessescited by 1
- 2025Leveraging External Factors in Household-Level Electrical Consumption Forecasting using Hypernetworkscited by 1
Built on by
Not cited yet.