Research on the prediction of bike sharing system’s demand based on linear regression model

Department of Mathematics, Brunel university, UB8 3PH London, UK

^* Corresponding author: 2058504@brunel.ac.uk

Abstract

This research developed a model of linear regression for forecasting the demand for shared bikes in a bike sharing system. By analyzing a dataset sourced from Kaggle, the study focuses on identifying the factors that have the most impact on bike demand and building a model based on these factors. The methodology involves data cleaning, creating dummy variables for categorical variables, and conducting exploratory data analysis. The features are rescaled, and the model building process includes recursive feature elimination and analysis of VIF and p-values. The outcome indicated that linear regression model accurately predicts bike demand based on various factors. This model can assist employers in adapting their business strategies, understanding customer expectations, and effectively managing bike-sharing systems. The findings contribute to the optimization and success of sustainable urban transportation, emphasizing the potential of bike sharing as an eco-friendly transportation option.