Using Stacking Ensemble Method for Rental Bike Prediction

Abstract

Rental bike platforms that improve mobility comfort are on the rise in major cities worldwide. One of the essential requirements for these rental bike systems is that bikes are available to end users at the specified time, reducing waiting time. Increased waiting time indicates that movement has been halted, implying that more efficiency can be gained. As a result, the city’s main priority is ensuring a steady supply of bicycles. It’s crucial to be able to forecast the number of bikes needed at each hour for this. This work look at alternative models for forecasting the bike count per hour needed to maintain a steady supply of bikes. Weather data (Temperature, Humidity, Wind speed, Dew point), the quantity of bikes hired every hour, and time information are all used to train the models. Filtering can also be used to exclude non-predictive parameters and rank features based on how well they predict outcomes. The effectiveness of the regression model was assessed using a testing set after they had been trained using repeated cross-validation. For the model Gradient Boosting Machine, the optimum R2 value is 0.96. The most significant predictors are also determined, as well as their relationships. Bike-sharing demand, data mining, predictive analytics, public bikes, regression. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.