From data to safer roads: predictive modelling and causal analysis of road fatalities in Australia

TL;DR

This study uses forecasting and causal analysis to understand and predict road fatalities in Australia, aiming to improve road safety policies.

Contribution

The study introduces a comparative analysis of forecasting models and identifies causal factors influencing road fatalities in Australia.

Findings

TBATS model excels in short-term road fatality predictions.

Vector Autoregression is best for medium- and long-term forecasts.

Older age groups and remote areas are key contributors to fatal crashes.

Abstract

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 online version contains supplementary material available at 10.1038/s41598-025-33744-7.

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