Data-Driven Mobility: Predicting Urban Bike-Share Usage through Historical Trends

Abstract

This project focuses on developing a bike-share usage forecasting tool using historical rental data and predictive analytics. By collecting and preprocessing data, and conducting exploratory analysis, usage patterns are identified. Implementing various predictive models, including time series and machine learning algorithms, demonstrates improved accuracy in forecasting bike-share usage. The tool supports city planners and operators in optimizing resource allocation and enhancing user satisfaction. It also suggests future research directions to further refine forecasting capabilities and operational efficiency in bike-share systems. The results demonstrate the effectiveness of the chosen methodologies in accurately predicting bike-share usage, with significant improvements over baseline models. The developed forecasting tool offers valuable insights for city planners and bike-share operators, enabling data-driven decision-making for better management of bike-share systems. The study also highlights potential real-time applications and suggests areas for future research to further enhance forecasting accuracy and operational efficiency.