Proactive Road Safety Monitoring with Connected Vehicle Data
Road safety monitoring has traditionally been a reactive process, relying on crash-record analysis after fatalities and injuries have already occurred. However, proactive identification of high-risk locations and dangerous driving behaviour before incidents occur is a critical but underexplored challenge.
This paper addresses this gap by using connected vehicle telemetry data from Greater Sydney, Australia, to detect and forecast near-miss risky driving events at the Local Government Area (LGA) level.
Risky driving is quantified through g-force thresholds (hard braking >0.6g, harsh cornering >0.47g, harsh acceleration >0.5g), and spatio-temporal heatmaps are constructed to identify high-risk zones.
Predictive Models Benchmarking
Eight predictive models are benchmarked across three families: ensemble learning (Random Forests, XGBoost, LightGBM), deep learning (LSTM, N-BEATS), and classical time-series methods (ARIMA, Exponential Smoothing, Prophet).
ARIMA achieves the lowest mean absolute error (MAE: 162.21), performing comparably to LSTM (MAE: 163.92) and outperforming all ensemble methods, with N-BEATS reaching an MAE of 180.75.
These results demonstrate that parsimonious time-series models are competitive with deep learning approaches when training data volume is limited.
Proactive Road Safety Interventions
The study highlights the potential of IoT-based connected vehicle data to support proactive road safety interventions.
Sydney's inner and western LGAs (CBD, Parramatta, Bankstown) are identified as persistent high-risk zones warranting targeted policy action.