This document discusses dynamic pricing and its use in ridesharing platforms. Dynamic pricing, also called surge pricing, involves adjusting prices based on supply and demand in real-time. The biggest challenges for platforms using dynamic pricing are forecasting demand accurately, maximizing revenue through predictive models, and predicting travel times. Machine learning algorithms using large datasets can help address these challenges by improving demand predictions and setting optimal dynamic prices. Joint learning models that combine demand forecasting with revenue-maximizing price optimization show promise for dynamic pricing.

1. A Presentation on:
Dynamic Surge Pricing and Its Relation to
Proactive Prediction of Supply and
Demand

2. What is dynamic pricing?
The process of determining a product's
value in commercial transactions in a
fluid manner depending on current
market conditions.
Key factors:

competitor pricing

time based pricing

supply and demand

market Conditions

geographical area

customer profile

Customer behavior analysis

3. Why dynamic pricing?

Profit maximization

Balance supply and demand

Customer understanding

Customer Satisfaction

4. History behind Dynamic Pricing

RyanAir revolutionized the airline industry in 1992 through
dynamic pricing system

Currently all airlines follow their model

This strategy also changed customer behavior

Concert, Sports and many other industries follow supply­
demand policy for ticket selling

5. Platforms using dynamic pricing

UBER

Lyft

Didi Chuxing

Ola

Grab

GoCatch

6. Challenges?

Forecasting demand

Maximizing revenue

Prediction of driving time

Regulations

7. Challenge: Forecasting demand
Key features:

Historical demand patterns

Real time information

Point of interest

Weather conditions
Solutions:

Big data

Machine learning algorithms
and statistical models

8. Challenge: Maximizing revenue
Key features:

Prediction of demand curve

Prediction of “true” customers

Customer behavior: “customers are myopic”
Solutions:

Big data and AI

Deterministic demand model

Setting higher minimum base price

Profit maximizing strategies: packages, discounts etc.

9. Challenge: Prediction of driving time
Features:

Geo­location information

Prediction of traffic
Solutions:

Big Data and AI to predict (example: Uber)

10. Reply from expert:
Question: What are the biggest challenges?
“The biggest challenge is a very positive challenge, which is the opportunity
to use machine learning to improve Uber's customer experiences. There's no
limit to the use of machine learning within Uber”
– Danny Lange, Head of Machine Learning, Uber

11. Dynamic Pricing Algorithms

Proportional pricing

Batch update pricing

Reinforcement learning

Stochastic learning/Joint learning

Game theory based (for extreme competitions)

12. Proportional pricing
P = Pbase
K D/S∗ ∗
P = Dynamic pricing
Pbase
= Base price
K = Factor
D = Demand
S = Supply
Advantages

Comes from “economics” point of view

Solves the problem of “supply and
demand” mismatch

Simplicity
Disadvantages

Choosing K becomes a trial & error
method

High demand/low supply can hurt
(example: Uber in NY at 31st
Night,
2015)

13. Results:

14. Batch update pricing
Pbase
= Pbase
− K (P∗ base
−Pmatched
)
Pbase
= Base price
K = Learning rate
Pmatched
= Matched price
Advantages

works on rounds, so highly
adaptive model

fast convergence

ensures customer satisfaction
Disadvantage

matching could be difficult

lowers profits

depends on survey data

15. Results:

16. Reinforcement Learning
Model:
 States, s (Pbase
, D, S)

Actions, a (P)

Reward, r (D/S, R)
Advantages

Apparently, maximizes revenue and
balances supply and demand

Stability
Disadvantages

Expensive in computation

Complex model

Agent based model
Learning: Q­learning
Q(st+1
,at+1
) = (1−αt
)Q(st
,at
)+αt
[R(st
,at
,
st+1
)+k*max [Q(st+1
, at
)]
K = discounted value, α = constant

17. Results:

18. Proposal: A Joint Learning Model
Objective:

Predict demand through learning

Set dynamic price through revenue maximization

19. Model
Predict demand through learning from historical data:
Dt+1
= Dt
(X, Pt
) + N
Dt+1
= new demand, X = set of features, N = Gaussian noise, Pt
= base price
# This Could be subject to both: 1)regression problem 2)recurrence problem
Dynamically setting surge price that maximizes revenue:
P*t+1
= argmax[Pt
*{Dt
(X, Pt
) + N}]
# Both statistical and probabilistic models can be used to set policy

20. Algorithm
For t in range(0,T): [T = a certain period, for example: one week]
1) Set base price and demand: Pt
= Pt
, Pt
:[Pl
, Ph
], Demand = Dt
2) Predict demand: Dt+1
= Dt
(X, Pt
) + N
3) Set policy: Qt+1
= P*t+1
= argmax[Pt
*{Dt
(X, Pt
) + N}]
4) if (P*t+1
= Pt
)
is True: Stop
else: Dt+1
= Dt
(X, P*t+1
) + N
Finish

21. Motivation

Research by MIT in 2001

Work by Columbia University for Singapore Airlines in 2012

Few more related literatures on dynamic pricing in sharing
economy

22. References

Besbes, Omar, and Assaf Zeevi. "Dynamic pricing without knowing the demand function: Risk bounds and
near­optimal algorithms." Operations Research 57.6 (2009): 1407­1420.

Farias, Vivek F., and Benjamin Van Roy. "Dynamic pricing with a prior on market response." Operations
Research 58.1 (2010): 16­29.

Wu, Tony, Anthony D. Joseph, and Stuart J. Russell. "Automated Pricing Agents in the On­Demand
Economy." (2016).

Bertsimas, Dimitris, and Georgia Perakis. "Dynamic pricing: A learning approach." Mathematical and
computational models for congestion charging (2006): 45­79.

Chen, M. Keith, and Michael Sheldon. "Dynamic Pricing in a Labor Market: Surge Pricing and Flexible
Work on the Uber Platform." EC. 2016.

Chen, Le, Alan Mislove, and Christo Wilson. "Peeking beneath the hood of uber." Proceedings of the 2015
ACM Conference on Internet Measurement Conference. ACM, 2015.

Cachon, Gerard P., Kaitlin M. Daniels, and Ruben Lobel. "The role of surge pricing on a service platform
with self­scheduling capacity." Manufacturing & Service Operations Management (2017).

23. THANK YOU