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| Main Author: | |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2602.19404 |
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| _version_ | 1866917287871643648 |
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| author | Eglinskas, Tomas |
| author_facet | Eglinskas, Tomas |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_19404 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | One Size Fits None: Modeling NYC Taxi Trips Eglinskas, Tomas Machine Learning Artificial Intelligence 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. |
| title | One Size Fits None: Modeling NYC Taxi Trips |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2602.19404 |