Application of Machine Learning in Predicting the Number of Bike Share Riders

Bongs Lainjo

doi:10.47747/ijbme.v3i4.865

Authors

Bongs Lainjo Cybermatic Internation

DOI:

https://doi.org/10.47747/ijbme.v3i4.865

Keywords:

Bike-Sharing Programs, User-Type, Monitoring Systems, Analytics, Predictive Models, Historical Data

Abstract

This study aims to investigate the factors influencing the demand for bike sharing to identify the variables that significantly predict the need for shared bicycles. The study aims to create more in-depth knowledge about bike-sharing models to enhance the ideas on designing, developing, implementing and utilization of bike-sharing models. A multiple regression model was used to model the demand for 703 Capital Bikeshare’s shared bikes in the USA. The variations in the need for the bikes were assessed based on certain variables, such as the total number of registered and unregistered bikers and renters. Linear regression analyses were conducted to determine the factors that statistically and significantly predict the number of bikes rented. Machine learning classifiers: Random Forest, Decision Trees, Nearest Neighbor and XGBoost were used to determine the most important predictors of bike demand and the data analyzed in SPSS V25, R and PYTHON. Increase in demand for shared bikes was attributed to factors such as increase in temperature (p =0.000), days of the week except the first day of the week (p < 0.05), the month of September (p = 0.036), spring season ((p = 0.000), fall seasons (p = 0.001) and humidity (p = 0.000). A significant decrease in the demand for shared bikes was observed on the first day of the week (p = 0.218) and days with strong winds (p = 0. 113). More people are likely to rent shared bikes on hot days, in September, during spring and fall seasons, on humid days, and all days of the week except on the first day of the week.

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