Proactive Road Safety Intervention in Australia: Predicting Risky Driving Hotspots from Connected Vehicle Data

Research proposes a shift from reactive to proactive road safety interventions by leveraging connected vehicle telemetry data to identify and forecast risky driving hotspots. This approach, exemplified in Greater Sydney, Australia, quantifies risky driving behaviors using g-force thresholds (hard braking, harsh cornering, harsh acceleration) to construct spatio-temporal heatmaps. The study aims to detect near-miss events at the Local Government Area level before actual incidents occur, representing a significant advancement in road safety monitoring.

Why it matters

This shift from reactive to proactive road safety management offers the potential to significantly reduce fatalities and injuries by anticipating and mitigating risks before incidents occur. It transforms how jurisdictions can approach infrastructure planning, resource allocation, and public safety initiatives, leading to more efficient and impactful interventions.

Key insights

• Traditional road safety monitoring is reactive, relying on post-incident analysis.
• Connected vehicle telemetry data can be used for proactive identification and forecasting of risky driving events.
• Risky driving is quantified using specific g-force thresholds for hard braking (>0.6g), harsh cornering (>0.47g), and harsh acceleration (>0.5g).
• Spatio-temporal heatmaps are generated to identify high-risk zones at the Local Government Area (LGA) level.
• The research benchmarks eight predictive models to forecast near-miss events.
• The focus is on detecting and forecasting near-miss risky driving events in Greater Sydney, Australia.

Source

arXiv — Computers and Society — https://arxiv.org/abs/2608.16913