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

arXiv: https://arxiv.org/abs/2608.16913 · HTML: https://mlsanity.com/paper/2608.16913/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 29 references that are in the map.
- First posted on arXiv 15 Jul 2026.

## Builds on (most cited first, 12 of 12)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [N-BEATS: Neural basis expansion analysis for interpretable time series forecasting](https://mlsanity.com/paper/1905.10437/) (2019)
- [Darts: User-Friendly Modern Machine Learning for Time Series](https://mlsanity.com/paper/2110.03224/) (2021)
- [Arterial incident duration prediction using a bi-level framework of extreme gradient-tree boosting](https://mlsanity.com/paper/1905.12254/) (2019)
- [Incident duration prediction using a bi-level machine learning framework with outlier removal and intra-extra joint optimisation](https://mlsanity.com/paper/2205.05197/) (2022)
- [Traffic Accident Risk Forecasting using Contextual Vision Transformers](https://mlsanity.com/paper/2209.11180/) (2022)
- [Motorway Traffic Flow Prediction using Advanced Deep Learning](https://mlsanity.com/paper/1907.06356/) (2019)
- [Traffic incident duration prediction via a deep learning framework for text description encoding](https://mlsanity.com/paper/2209.08735/) (2022)
- [Traffic congestion anomaly detection and prediction using deep learning](https://mlsanity.com/paper/2006.13215/) (2020)
- [Graph modelling approaches for motorway traffic flow prediction](https://mlsanity.com/paper/2006.14824/) (2020)
- [Patterns of near-crash events in a naturalistic driving dataset: applying rules mining](https://mlsanity.com/paper/2201.06523/) (2022)
- [Spatial Association Between Near-Misses and Accident Blackspots in Sydney, Australia: A Getis-Ord Gi^* Analysis](https://mlsanity.com/paper/2506.03356/) (2025)

## Abstract (arXiv)

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.