Briefing
In the modern era, cities worldwide face the challenges of traffic congestion, environmental concerns, and the promotion of healthier lifestyles. Bike sharing systems have emerged as a pioneering solution to these pressing issues, seamlessly integrating technology to revolutionize traditional bike rentals. With over 500 bike-sharing initiatives globally, urban areas such as Hangzhou, Paris, London, New York City, and Montreal have embraced this innovation. Taking Washington’s Capital Bikeshare as a prime example, this study draws from its comprehensive dataset to decode the dynamics of daily bike rentals.
Our analysis, conducted using SAS, began with data preprocessing and exploration, diving deep into patterns influenced by factors such as weather, seasonality, and time of day. The subsequent stages involved rigorous statistical modeling to predict daily bike rental counts. Through these data-driven predictions, we aim to provide stakeholders with actionable insights for operational enhancements and demand forecasting.
Eager to navigate through our detailed findings and insights? Dive deeper into the full study on Medium.