Supervisor
Dr. Muhammad Iqbal
Programme
BSc (Hons) in Computing in IT
Subject
Computer Science
Abstract
Dublin faces increasing traffic congestions with over 76% of Irish residents relying on private cars for daily transport, well above the EU average (MacCarthaigh, 2022). This contributes to increased greenhouse gas emissions, challenging Ireland’s goals to reduce emissions by 55% by 2030. 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 corridors 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.
Date of Award
2025
Full Publication Date
2025
Access Rights
open access
Document Type
Poster
Resource Type
other
Recommended Citation
Burtinik Urueta, M., & Yu, M.
(2025) Data Driven Public Transport Planning in Dublin : A Clustering and Forecasting Approach CCT College Dublin.
DOI: https://doi.org/10.63227/652.199.44