The Growth of E-Bike Use: A Machine Learning Approach
Aditya Gupta Samarth Chitgopekar Alexander Kim Joseph Jiang Megan Wang Christopher Grattoni
We present our work on electric bicycles (e-bikes) and their implications for
policymakers in the United States. E-bikes have gained significant popularity
as a fast and eco-friendly transportation option. As we strive for a
sustainable energy plan, understanding the growth and impact of e-bikes is
crucial for policymakers. Our mathematical modeling offers insights into the
value of e-bikes and their role in the future. Using an ARIMA model, a
supervised machine-learning algorithm, we predicted the growth of e-bike sales
in the U.S. Our model, trained on historical sales data from January 2006 to
December 2022, projected sales of 1.3 million units in 2025 and 2.113 million
units in 2028. To assess the factors contributing to e-bike usage, we employed
a Random Forest regression model. The most significant factors influencing
e-bike sales growth were disposable personal income and popularity.
Furthermore, we examined the environmental and health impacts of e-bikes.
Through Monte Carlo simulations, we estimated the reduction in carbon emissions
due to e-bike use and the calories burned through e-biking. Our findings
revealed that e-bike usage in the U.S. resulted in a reduction of 15,737.82
kilograms of CO2 emissions in 2022. Additionally, e-bike users burned
approximately 716,630.727 kilocalories through their activities in the same
year. Our research provides valuable insights for policymakers, emphasizing the
potential of e-bikes as a sustainable transportation solution. By understanding
the growth factors and quantifying the environmental and health benefits,
policymakers can make informed decisions about integrating e-bikes into future
energy and transportation strategies.