Supervisor
Dr Muhammad Iqbal
Programme
BSc (Hons) in Computing in IT
Subject
Computer Science
Abstract
Traffic congestion is an increasing challenge in modern cities, impacting transportation efficiency and urban sustainability. This project focuses on short-term traffic volume forecasting, using 15-minute Sydney Coordinated Adaptive Traffic System (SCATS) data collected from the State of Victoria, Australia. The study compares two forecasting approaches: SARIMA, a statistical time-series model, and XGBoost, a decision-tree machine-learning model. Historical traffic data from 2022 to 2024 was analysed to identify traffic patterns, seasonal trends, and peak traffic periods. The models were evaluated for their effectiveness in handling urban traffic behaviour. The results of this study aim to support smarter traffic management, infrastructure planning, and decision-making within smart city environments by improving the efficiency of traffic flow.
Date of Award
2026
Full Publication Date
2026
Access Rights
open access
Document Type
Undergraduate Project
Resource Type
bachelor thesis
Recommended Citation
Dierings, D., & Fontanive Marques, F.
(2026) A comprehensive study of SARIMA and XGBoost models for short-term traffic volume forecasting at a Melbourne intersection (Victoria), Australia CCT College Dublin.
DOI: https://doi.org/10.63227/652.299.140