Algorithm for making automated decisions for Lyft's driver positioning system.

1. Architecting a real-time
optimization framework for
Driver Positioning
Hao Yi Ong
w/ many others at Lyft
INFORMS Annual Meeting | Seattle | October 22, 2019

2. Overview
ResultsFormulationProblem

3. Problem

4. Dynamic pricing heatmap for drivers
Prime Time (PT)

5. Personal Power Zones (PPZ) product
Pre-empt general market imbalance
and help with events or demand
spikes
Algorithmically coordinate drivers
when whole cities light up with rider
demand
De-conflicting drivers Early signaling

6. Give a ride, get your bonus
#1

7. Drive into the purple zone,
guarantee a bonus on your next
ride.
#2

8. Drive into the pink zone, guarantee
an even bigger bonus
#3

9. Formulation

10. PT problem
The PT model maps market conditions (supply and demand) to the general notion of value.
// value; e.g., rides
// match supply/demand
// limit price
fixed parameter:
treatable supply
untreatable supply
price-adjusted
demand
reserved supply

11. PT problem
The PT model maps market conditions (supply and demand) to the general notion of value.
MKT model
demand by gh6
supply by gh6
value

12. PPZ problem
Supply and supply allocation are the PPZ decision variables (in addition to PT). The PPZ
model repositions supply to maximize value.
PPZ model
MKT model
forecasted demand by gh6
supply by gh6
value
iteratively refine
supply allocation
“feedback”

13. PPZ-induced value
// move supply
// allocation constraints
decision variable:
treatable supply
PPZ problem
Supply and supply allocation are the PPZ decision variables (in addition to PT). The PPZ
model repositions supply to maximize value. (Omitted: various other business constraints.)
decision variable:
supply allocation
initial treatable
supply

14. Results

15. Assumptions
● Drivers don’t reposition themselves for decision look-ahead
● Driver compliance exponentially decays as travel time increases (fitted)
● When drivers comply, they move to destination by the next time step
Methodology
● Historically accurate data importance-sampled over a quarter over top-10 cities
● “Fractional drivers” rounded down to the nearest integer to compute “actual value”
Results (varies by city)
● 1—4% incremental rides
● 0.2—1% incremental bookings
Numerical experiments

16. Methodology
● Time-split experiments infeasible due to product experience
● User-split testing is the least bad option
● Impact estimates derived from causal inference methods
Results (varies by city)
● ~0.3% incremental bookings
● Overall, drivers prefer PPZ over PT
● PPZ rolled out to virtually everywhere
User-split experiments

17. Work in-progress in collaboration with many teams at Lyft — join us!

18. Reach out!
haoyi@lyft.com