Supervisor

Dr. Kashif Quershi

Programme

MSc in Data Analytics

Abstract

This study explores the application of machine learning models to predict demand in the Dublin bike-sharing scheme. A comprehensive literature review examines prior work on sustainable urban mobility, the evolution of bike-sharing schemes, the application of machine learning in predictive analytics and relevant case studies. Secondary data were gathered from publicly available sources, pre-processed and merged into a single, unified dataset. Exploratory data analysis was conducted before preparing the data for modelling to assess the data quality, with a focus on missing values.

Four predictive machine learning models – Linear Regression (LR), Decision Tree (CART), Random Forest (RF) and Gradient Boosting Machine (GBM) were developed and assessed using quantitative metrics, including Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Square Error (RMSE) and Coefficient of Determination (R2). Hyperparameter tuning was applied to optimise the best performing model, which was further tested on a subset of working days, weekends and public holidays. Results revealed that RF delivered the highest accuracy and effectively captured patterns within the data, with the feature hour being the most influential predictor. While the RF provides robust and explainable results, future research could benefit from more advanced methods such as Neural Networks (NN) and Graph Convolutional Networks (GCN), which may be better at capturing complex spatiotemporal patterns in bike-sharing demand.

Date of Award

2025

Full Publication Date

2025

Access Rights

open access

Document Type

Capstone Project

Resource Type

thesis

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