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

A new study using connected vehicle telemetry data from Greater Sydney, Australia, found that ARIMA, a classical time-series model, achieves the lowest mean absolute error (MAE: 162.21) in forecasting near-miss risky driving events at the Local Government Area (LGA) level, outperforming deep learning and ensemble methods. The research, released on arXiv (2608.16913v1), identifies Sydney's inner and western LGAs (CBD, Parramatta, Bankstown) as persistent high-risk zones, demonstrating the potential of IoT-based connected vehicle data for proactive road safety interventions.

arXiv:2608.16913v1 Announce Type: new Abstract: Road safety monitoring has historically been reactive, relying on crash-record analysis after fatalities and injuries have already occurred. Proactive identification of high-risk locations and dangerous driving behaviour before incidents occur is a critical but underexplored challenge. This paper addresses this gap 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. 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. The study highlights the potential of IoT-based connected vehicle data to support proactive road safety interventions, with Sydney's inner and western LGAs CBD, Parramatta, Bankstown identified as persistent high-risk zones warranting targeted policy action.