The city of Chicago's bike-sharing system, Divvy, provides residents and visitors with a sustainable and convenient transportation option. Users can rent bikes for short periods and return them to any of the many stations located throughout the city. As bike-sharing systems grow in popularity, understanding usage patterns and trends becomes crucial for optimizing performance and enhancing the overall user experience.
This project aims to explore the bike-sharing patterns in Chicago using data from the Divvy bike-sharing system. We will analyze the data to identify trends and patterns in bike usage, including the most popular stations, times of day, and days of the week. We will also examine the relationship between bike usage and weather conditions, such as temperature, precipitation, and wind speed. By gaining a deeper understanding of bike-sharing patterns in Chicago, we can provide insights that can inform the development of more efficient and effective bike-sharing systems.
- Conduct time series forecasting for all major stations with hourly granularity.
- Analyze the correlation between bike usage and weather factors, including temperature, precipitation, and wind speed.