Divvy Bike-Share.zip

This study builds a simple and accurate forecasting model to predict monthly <b>Divvy bike-share trip volumes</b> and <b>average trip durations</b> in Chicago. Using data from January to December 2024—including trip records, weather conditions, and neighborhood characteristics—the researchers created nine features (like weekend flags and past usage data) for each community area.They trained a <b>LightGBM model</b> in R and tested it using two methods to avoid bias and ensure it works well across different neighborhoods. The final model predicted trip counts with an error of only <b>8.4%</b> and trip durations with <b>2% error</b>. Most of the prediction power came from the <b>one-month lag</b> in usage.The model proves that a <b>simple, interpretable approach</b> (not deep learning) can give <b>urban planners accurate insights</b> for bike-share demand forecasting.