One Size Fits None: Modeling NYC Taxi Trips

TL;DR

This study compares the predictability of tips in traditional taxis and app-based ride-sharing in NYC, revealing that traditional tips are highly predictable while app-based tips are largely random, emphasizing the need for specialized models.

Contribution

The paper demonstrates that a universal tipping model is ineffective, highlighting the importance of category-specific models due to differing data patterns and the impact of Simpson's paradox.

Findings

Traditional taxi tips are highly predictable with $R^2 \\approx 0.72$

App-based tips are difficult to predict with $R^2 \\approx 0.17$

A combined model fails to accurately predict tips for individual categories

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.