Articles
Publication Date: 2026
Scientific Reports (20452322)16(1)
This study uses advanced time-series forecasting and causal modelling techniques to examine long-term patterns in Australian road traffic fatalities. Four statistical approaches were assessed: Holt-Winters, Theta, TBATS, and Vector Autoregression, with each offering strengths across different forecasting horizons. TBATS provided the most reliable short-term predictions, while Vector Autoregression performed best for medium- and long-term projections. A causal analysis using a random-effects panel model identified several key contributors to fatal crash risk, including older age groups, remote and outer-regional settings, nighttime periods, and high-speed environments. In contrast, younger adults and single-vehicle crashes were associated with lower fatality likelihood. Overall, the results demonstrate the value of flexible time-series techniques and panel data methods for guiding evidence-based road safety policy, targeted interventions, and infrastructure planning. © The Author(s) 2025.
Publication Date: 2025
Case Studies on Transport Policy (22136258)20
In Australia, road crash injuries continue to be a serious public health issue. Machine learning is used in this study to analyse injury data from road crashes between 2011 and 2021 that was taken from the national hospitalized injury database. We investigate how the number of injuries and duration of stay for road users are affected by variables such as gender, age, seasonal variation, collision type, and location (urban vs. regional). Road safety measures are informed by patterns and relationships found in the data by machine learning models. Hospitalizations have been trending upward between 2011 and 2019, with a pause in 2020 due to COVID-19 lockdowns. In all categories, men sustain more injuries than women, though the number varies according to age and geography. The type of road user also affects collision patterns. The time-series projections demonstrate that the goal of zero fatalities in 2050 will not be achieved under the business-as-usual scenario. The findings highlight the necessity of focused interventions predicated on collision trends and demographics. This includes better infrastructure design, increased surveillance, and customized safety measures. © 2025 The Author(s)
Publication Date: 2025
Machine Learning with Applications (26668270)22
Road traffic injuries continue to pose a significant public health challenge in Australia, with pedestrians representing one of the most vulnerable road user groups. Accurate prediction of injury severity, particularly fatal outcomes, is essential for improving road safety interventions and resource allocation. This study applies advanced machine learning techniques to predict pedestrian crash severity using national hospitalization and mortality data collected from 2011 to 2021. The analysis focuses on addressing class imbalance, a common issue in injury data by evaluating the impact of several data balancing methods, including SMOTE, ADASYN, Random Oversampling (ROS), and Threshold Moving. We implement and compare four supervised learning algorithms: Logistic Regression, Support Vector Machine (SVM), Decision Tree, and XGBoost. Model performance is assessed using F1-score and macro-accuracy, with a focus on the minority (fatality) class. Results show that XGBoost combined with Threshold Moving achieves the highest performance, yielding an F1-score of 72% for fatality classification and a macro-accuracy of 84%. Additionally, feature importance analysis using SHAP values reveals age, gender, road user type, and crash location as key predictors of injury severity. The study highlights the critical role of data balancing strategies in enhancing predictive accuracy for rare but high-impact outcomes. These findings provide actionable insights for transport authorities and policymakers seeking to develop data-driven, targeted safety measures to protect pedestrians and reduce the severity of crash outcomes. © 2025 The Author(s).
