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Rideshare and ride-pooling platforms use artificial intelligence-based matching algorithms to pair riders and drivers. However, these platforms can induce unfairness either through an unequal income distribution or disparate treatment of riders. We investigate two methods to reduce forms of inequality in ride-pooling platforms: by incorporating fairness constraints into the objective function and redistributing income to drivers who deserve more. To test these out, we use New York City taxi data to evaluate their performance on both the rider and driver side. For the first method, we find that optimizing for driver fairness out-performs state-of-the-art models in terms of the number of riders serviced, showing that optimizing for fairness can assist profitability in certain circumstances. For the second method, we explore income redistribution as a method to combat income inequality by having drivers keep an $$r$$ fraction of their income, and contribute the rest to a redistribution pool. For certain values of $$r$$, most drivers earn near their Shapley value, while still incentivizing drivers to maximize income, thereby avoiding the free-rider problem and reducing income variability. While the first method is useful because it improves both rider and driver-side fairness, the second method is useful because it improves fairness without affecting profitability, and both methods can be combined to improve rider and driver-side fairness.
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Improving Rider Safety Using QR Code & Fingerprint Biometrics
Transport Network companies (TNCs) have changed the way we travel in the last five years where a rider can book a ride using her smartphone. However, TNC doesn't provide any robust mechanism to validate the driver or the rider before the ride. This has led to many violent incidents ranging from assault, kidnap of the riders by fake ride-hailing drivers. The most recent one that shook the entire nation is the murder of a USC student when the rider got into the wrong car thinking that it is her Uber [1]. To address this problem, we have proposed a solution that adds an extra security layer in authenticating both rider and driver before initiating a ride. In this solution, both rider and driver will authenticate themselves using technologies like QR Code and fingerprint biometrics supported by modern smartphones before they take the ride.
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- PAR ID:
- 10161193
- Date Published:
- Journal Name:
- 2019 IEEE 10th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON)
- Page Range / eLocation ID:
- 0141 to 0144
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/UEMCON47517.2019.8993069
