Ridesharing
summary
Presents a concrete model: a closed queuing network with M vehicles, N zones, Poisson origin–destination arrivals and state-dependent acceptance via upfront prices; uses convex programming, a fluid-limit and an elevated flow relaxation with circulation constraints to get provable bounds. Main claims: simple state-independent prices are near-optimal with approximation factor about 1 + (N−1)/M and dynamic pricing acts as an automatic calibrator robust to demand uncertainty. Open pieces include pooling, bursty arrivals, driver incentives and reservation design.
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chapters (16)
- 0:00Introduction
- 6:00Dynamic pricing
- 7:00Fluid vs discrete
- 8:40Prime time
- 10:10Logistics
- 11:25Marketplaces
- 12:55Ecosystem
- 14:25Competition
- 17:05Rebalancing
- 19:25Closed queuing network
- 23:20Ridesharing platforms
- 24:35Control levers
- 27:20Pooling
- 28:20Theoretical Models
- 36:50Strategic Interaction
- 55:10Summary
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