Supervisor
Dr. Muhammad Iqbal
Programme
BSc (Hons) in Computing in IT
Subject
Computer Science
Abstract
Dublin has been experiencing severe traffic congestion due to rapid economic and population growth, with residents losing an average of 158 hours per year in traffic during rush hour (Europe Data, 2025). A 2022 European Commission study found that 76% of Irish people use a car as their primary mode of transport on a typical day—an 8% increase from 2019, compared to the EU average of 47% (MacCarthaigh, 2022).
This project proposes a data-driven approach to identifying current transport accessibility gaps and forecasting future population growth across Dublin to support sustainable infrastructure development. Using Ireland’s Census data, an unsupervised method was applied to cluster EDs based on similarities in population dynamics. Forecasts were generated in 5-year intervals, revealing key growth across Dublin using a clustered VAR model.
These findings can offer actionable insights to policymakers, urban planners, transport authorities and private sector stakeholders. The combined models provide a scalable framework for demand forecasting and transport prioritisation relevant today and at transport project completion.
Date of Award
2025
Full Publication Date
2025
Access Rights
open access
Document Type
Undergraduate Project
Resource Type
bachelor thesis
Recommended Citation
Burtinik Urueta, M., & Yu, M.
(2025) Data-Driven Public Transport Planning for Dublin: A Clustering and Forecasting Approach CCT College Dublin.
DOI: https://doi.org/10.63227/652.199.45