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Machine learning and algorithms used for surge pricing
Surge pricing (dynamic pricing in response to demand) will be one in which prices fluctuate under certain conditions and the optimum price is set in real time in response to consumer demand and supply conditions. To achieve this, various machine learning and algorithms are used, with demand forecasting and market analysis techniques playing a particularly important role. The main machine learning models and algorithms used in surge pricing are described below.
1. demand forecasting models:
– Time series analysis: the basis of surge pricing is to forecast fluctuations in demand, and analyses based on time series data, such as ARIMA models and exponential smoothing models, as described in ‘Time series analysis using Prophet’ and elsewhere, are often used. These models capture trends and seasonality from historical data and forecast short-term demand.
– Recurrent neural networks (RNNs): LSTMs, as described in ‘Overview, algorithms and implementation examples of LSTMs’ and GRUs (Gated Recurrent Units), as described in ‘Overview, algorithms and implementation examples of GRUs’, are excellent for forecasting continuous time-series data such as demand data. Companies such as Uber and Lyft use RNNs, described in ‘RNN overview, algorithms and implementation examples’, to predict demand for drivers and passengers.
2. regression models:
– Linear and polynomial regression: these are simple models, but are useful for forecasting basic elements of surge pricing, for example, demand can be forecast using variables such as temperature, day of the week and events.
– Decision trees, random forests and gradient boosting: these regression models are suitable for forecasting demand in complex environments where multiple variables are involved and many factors are taken into account to capture specific peaks in demand.’ See also ‘For an overview of decision trees and examples of applications and implementations’.
3. reinforcement learning:
– Q-learning or Deep Q-Networks (DQN): reinforcement learning is applied to models for dynamically adjusting prices. Demand and supply conditions are regarded as the environment and the agent learns to set prices; Uber, for example, uses reinforcement learning based on demand and supply data to adjust prices in real time.’ See also, e.g., ‘Overview of Q-Learning and Examples of Algorithms and Implementations’, ‘Overview of Deep Q-Network (DQN) and Examples of Algorithms and Implementations’.
– Multi-armed Bandit Problem: The problem of choosing between multiple pricing options to maximise profit, applied to dynamic pricing decisions.’ See also ‘Overview of the multi-armed bandit problem and examples of applied algorithms and implementations’.
4. optimisation algorithms:
– Linear Programming: used to optimise pricing for profit maximisation. The goal of surge pricing is to maximise profit while maintaining a balance between supply and demand.’ See also ‘Overview of Linear Programming and Algorithms and Examples of Implementations’.
– Gradient Descent: often used in training neural networks to find the best parameters, especially when predicting demand fluctuations in response to price changes.’ See also ‘Overview of the Natural Gradient Method and Examples of Algorithms and Implementations’.
5. clustering and segmentation:
– K-means clustering: used for user segmentation. For example, clusters can be created for each user attribute and different pricing strategies can be applied to each cluster to enable flexible pricing.’ See also ‘Overview of k-means, applications and implementation examples’.
– Analysis of user behaviour: price sensitivity is estimated based on the behavioural history of each user. Demand can be adjusted through pricing strategies based on user attributes.
6. demand elasticity analysis:
– Price elasticity models: models that measure the response to changes in demand and price, analysing the impact of pricing. It models how much demand changes when prices change and is a key indicator in setting optimal prices.
– Revenue management: the airline and hotel industries, for example, use revenue management techniques together with surge pricing to maximise revenue by adjusting prices in real time in response to demand.
Surge pricing can significantly improve revenues at times of high demand, but can also frustrate users. It is therefore essential to optimise machine learning models and algorithms to improve forecasting accuracy and provide fair prices to users.
implementation example
As an example of a basic implementation of surge pricing, code is shown to build a demand forecasting and price optimisation system in Python. The example combines both a demand forecasting model and a price optimisation algorithm to dynamically change prices under certain conditions.
Libraries used:
- pandas: data management and manipulation
- numpy: numerical computing
- sklearn: building machine learning models
- scipy: performing optimisation
Example implementations:
import pandas as pd
import numpy as np
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
from scipy.optimize import minimize
# Creation of sample data
data = pd.DataFrame({
'demand': np.random.randint(50, 200, 100), # Demand (e.g. randomly generated)
'price': np.random.randint(10, 100, 100), # Price (randomly generated as an example)
'event': np.random.choice([0, 1], 100), # Availability of events
'day_of_week': np.random.choice(range(7), 100) # day of the week
})
# Feature and target setting
X = data[['price', 'event', 'day_of_week']]
y = data['demand']
# Split into training and test data.
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
# Building a linear regression model.
model = LinearRegression()
model.fit(X_train, y_train)
# predictive function
def predict_demand(price, event, day_of_week):
return model.predict([[price, event, day_of_week]])[0]
# Definition of price optimisation functions.
def optimize_price(event, day_of_week):
# Functions to maximise revenue (Revenue).
def revenue(price):
demand = predict_demand(price, event, day_of_week)
return -price * demand # Reversed sign for revenue maximisation
# Optimisation run
result = minimize(revenue, x0=50, bounds=[(10, 100)]) # Initial value 50, price range 10-100.
optimal_price = result.x[0]
return optimal_price
# Calculate the optimum price under certain conditions.
event = 1 # As an example, assume that an event is occurring
day_of_week = 5 # As an example, let's say it is Friday.
optimal_price = optimize_price(event, day_of_week)
print(f"optimum price: {optimal_price:.2f}")Code description:
- Data generation: generate sample data. This will be a dataset containing factors such as price, demand, events and days of the week.
- Demand forecasting model: a linear regression model is used to construct a demand forecasting model. It uses price, events and days of the week as characteristic quantities.
- Demand forecasting function: a function predict_demand is defined to forecast demand based on price, presence of events and days of the week.
- Price optimisation function: create an optimise_price function to find the optimum price to maximise revenue. This function uses Scipy’s minimize to search for the price at which revenue is maximised.
- Calculate and output the optimal price: find and output the optimal price for a specific condition (e.g. a Friday with an event).
Key points of implementation:
- Accuracy of demand forecasting models: more accurate models can provide more reliable demand forecasts. Models such as Ridge regression, Lasso and random forests can be considered.
- Price constraints: using bounds in minimize sets a price range and provides realistic pricing.
- Dynamic pricing: a mechanism is needed to use real-time data to forecast demand and continuously update the optimal price.
Application examples
Surge pricing has been introduced in a number of industries and has been particularly effective in industries where supply and demand fluctuate widely. Typical applications of surge pricing are described below.
1. car-delivery services (e.g. Uber, Lyft):
– Summary: Car-hailing services such as Uber and Lyft have introduced surge pricing, which automatically increases prices when the supply of vehicles cannot keep up with an increase in the number of users. Demand can be temporarily boosted by weather, time of day or certain events (concerts, sporting events), at which point prices are increased.
– Purpose: By increasing prices, the aim is to encourage users to use less during peak hours and to increase supply by rewarding drivers more. It would also allow the service to be concentrated in areas where there is a shortage of drivers.
2. the airline industry:
– Summary: Airlines also use dynamic pricing, a form of surge pricing, which is a ‘Revenue Management’ approach. Ticket prices are increased on days of high demand (e.g. holidays, New Year holidays, etc.) and prices are set even higher on flights with low seat availability.
– Purpose: to minimise seat availability and maximise revenue. In addition, historical sales data and demand forecasting models will be used to adjust optimal prices in real time to increase revenues on heavily used flights.
3. hotel industry:
– Summary: In hotels, it is common for prices to rise during seasons and events when demand for accommodation is high. Surge pricing is used to dynamically change the price of rooms to coincide with events, holidays and tourist seasons.
– Purpose: Dynamic pricing is essential to ensure maximum revenue while reducing vacancies, especially in tourist destinations and resorts where accommodation demand fluctuates widely.
4. food delivery services (e.g. Uber Eats, DoorDash):
– Summary: Surge pricing is also applied by food delivery services during peak times and bad weather. For example, delivery prices are increased during lunch and dinner hours and on rainy days, as demand for deliveries increases.
– Purpose: To achieve a stable supply of services by adjusting rates to meet rising demand and incentivising delivery partners to operate during peak times.
5. event ticket sales (sports, concerts, etc.):
– Summary: Dynamic pricing has been introduced in sports event and concert ticket sales, where ticket prices increase for more popular events. Demand for each event is predicted from historical data and prices are changed over the sales period.
– Purpose: The aim is to generate maximum revenue from events with high demand and to reduce price fluctuations on the resale market. This is expected to ensure that tickets reach more fans while keeping official prices at a reasonable level.
6. retail (especially e-commerce):
– Summary: Amazon and other major retailers change product prices in real time in response to product demand and competitors’ prices. On certain events, such as Black Friday or the Christmas season, prices may be temporarily raised or, conversely, drastically reduced in order to clear inventory.
– Purpose: The aim is to manage inventory and maximise profits. Price fluctuations enable the balancing of supply and demand and the sale of goods at the right time.
7. power industry:
– Summary: In electricity supply, prices also rise, particularly in summer and winter when demand for electricity is higher. By linking to smart meters, it is possible to monitor electricity usage in real time and raise prices when demand reaches peak levels, thereby curbing consumption.
– Purpose: The objective is to reduce excessive electricity consumption during peak periods and stabilise electricity supply. It provides an incentive for consumers to conserve electricity and contributes to stabilising overall electricity demand.
Surge pricing has been widely introduced as a method to maximise revenues and stabilise the supply of services by reflecting fluctuations in demand in real time. It enables companies to allocate resources efficiently while maintaining security of supply and balancing demand, but its application also requires careful judgement in terms of the impact of sudden price fluctuations on consumers and fairness.
reference book
Describes useful reference books on machine learning and algorithms for surge pricing and dynamic pricing.
1. “Pricing and Revenue Optimization” by Robert L. Phillips
2. “Revenue Management and Pricing: Case Studies and Applications” by Irena Bakhramova and Robert Klein
3. “Dynamic Pricing and Automated Resource Allocation for Complex Information Services” by Yu Zheng and Robert L. Grossman
4. “Machine Learning for Asset Managers” by Marcos López de Prado
5. “Introduction to Revenue Management for the Hospitality Industry: Principles and Practices for the Real World” by Kimberly A. Tranter, Trevor Stuart-Hill, and Juston Parker
6. “Algorithms to Live By: The Computer Science of Human Decisions” by Brian Christian and Tom Griffiths
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