Bike Sharing Systems (BSS)
Many cities and governments have been trying to focus on decreasing CO2 emissions, while also trying to control and understand mobility flows within their sphere of influence. Therefore, Bike Sharing Systems (BSS) were implemented around the world, and their popularity has been increasing due to environmental issues, pricing and convenience, creating a social trend. The BSS is essentially a type of transportation rental business that can be used as a supplement to the public transportation system. The data that has been collected from the users of the BSS, can be used to forecast future demand and analyse trends.
This project will use bike sharing datasets provided by UCI, Kaggle and other machine learning repositories. The main goal of the project will be to conduct a trend analysis and produce a model that can forecast demand while also being versatile enough to incorporate similar real datasets. The datasets contain a variety of attributes that take into account not only the rider's details but the environment and surrounding variables.
The project will mostly revolve around reviewing and comparing available, and applicable Machine Learning (ML) techniques using appropriate data and features to produce new findings. Through this project we will gain a clear understanding about statistical machine learning techniques, cross validation, data and report analysis, regression models and other ML techniques.
The BSS project was developed as a capstone project for the Univertity of Canberra, this means that the project's idea cannot be used. The work may be referenced with the citation below.
Johnson, Chen, Li, Williams (2022). Forecasting Demand for Bike Sharing Systems and Analysis with Python (Version 0.4.5) [Computer software]. https://github.com/GreenMachine582/BSS-MachineLearning