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Uber’s AI predicts that ride demand in a particular area will increase significantly in the next 20 minutes. How would you design an AI system that predicts demand and proactively moves drivers to that area?
Prediction is the easy half. The value is in the allocation, and most candidates stop before they get there.
1. Forecast both sides, per cell per bucket
- H3 hexagons × 5-minute buckets, forecasting rides requested and drivers available
- Features: the same hour last week, weather, events, traffic, and live app-open sessions
- Short horizons need current state; long horizons need seasonality
2. What matters is the gap
- Rank cells by expected unmet demand, which is demand minus reachable supply
- A surging cell that already has forty idle drivers needs nothing from you
3. Turn the forecast into assignments
- For each driver, the expected value of moving versus staying, minus travel time and fuel
- Solve it as min-cost flow across drivers and cells, not as a greedy nudge per driver
- Greedy sends everyone to the same hex and creates the opposite problem
4. Drivers are people, not units of supply
- Incentives and heatmaps, never orders — model the probability they accept
- Respect end-of-shift, home direction and rest; an ignored nudge is worse than none
5. Do not stampede
- Move a fraction of the nearby drivers, then re-solve a few minutes later
- Closed-loop correction beats one confident dispatch
6. Evaluate on the marketplace
- Geo-based A/B tests, because moving drivers changes the outcome for everyone in the city
- Track unfulfilled requests, pickup ETA and driver earnings per hour together
The principle: A demand forecast is only worth what the allocation on top of it earns. Optimise the assignment, not the RMSE.
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