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

Summary:This research proposes a proactive approach to road safety by leveraging connected vehicle telemetry data from Greater Sydney, Australia. By quantifying risky driving behaviors such as hard braking, harsh cornering, and rapid acceleration, and constructing spatio-temporal heatmaps, the study identifies high-risk zones for potential accidents. The results demonstrate that the ARIMA model outperforms more complex models like LSTM and ensemble methods in terms of prediction accuracy. This work high

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Background and Challenges

Traditional road safety monitoring methods are reactive, analyzing accidents after they occur. However, this approach is insufficient for effective accident prevention. To address this, the study proposes a proactive intervention method based on connected vehicle data to identify high-risk driving areas and dangerous behaviors in advance.

Data and Methods

The research team collected connected vehicle telemetry data from Greater Sydney, Australia, and defined the following thresholds for risky driving behaviors:

• Hard braking > 0.6g
• Harsh cornering > 0.47g
• Rapid acceleration > 0.5g

By constructing spatio-temporal heatmaps, the researchers identified high-risk zones and benchmarked eight predictive models:

• Ensemble methods: Random Forest, XGBoost, LightGBM
• Deep learning methods: LSTM, N-BEATS
• Classical time-series methods: ARIMA, Exponential Smoothing, Prophet

Key Findings

1. Superiority of ARIMA: The ARIMA model achieved the highest prediction accuracy with a Mean Absolute Error (MAE) of 162.21, outperforming more complex models like LSTM (MAE: 163.92) and ensemble methods.
2. Competitiveness of Time-Series Models: In scenarios with limited data, parsimonious time-series models can compete with deep learning approaches.
3. Identification of High-Risk Zones: Inner and western LGAs of Sydney (CBD, Parramatta, Bankstown) were identified as persistent high-risk zones, indicating the need for targeted policy interventions.

Technical Highlights

• Data-Driven Proactive Intervention: The study demonstrates the potential of connected vehicle data to enhance road safety through proactive measures.
• Model Comparison and Optimization: The comparison of various predictive models highlights the effectiveness of time-series models in specific scenarios.
• High-Risk Zone Identification: The findings provide valuable insights for urban traffic management, aiding in the development of more effective safety policies.

Industry Impact and Developer Recommendations

1. Policy Making for Traffic Management: Urban traffic authorities can utilize the methods and results of this study to formulate more effective road safety policies.
2. Application of Connected Vehicle Data: Connected vehicle service providers can explore more data-driven application scenarios based on this research.
3. Model Selection and Optimization: Developers should choose appropriate predictive models based on data characteristics and application scenarios to avoid unnecessary complexity.

Conclusion

This study showcases the significant potential of connected vehicle data in improving road safety and provides a scientific basis for urban traffic management. As data collection technologies advance and models are further optimized, proactive road safety intervention methods will become more precise and efficient.

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Source: ArXiv Machine Learning (cs.LG) (2026-08-20)

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Tags: #Connected Vehicles #Road Safety #Time-Series Analysis #Predictive Modeling #Intelligent Transportation