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

Author1, Author2, Author3, Author4, Author5

predictive modelingroad safetyconnected vehiclesIoT

2608.16913

Builder Relevance

80%

2h ago

Abstract

This paper explores proactive identification of high-risk driving locations using connected vehicle data.

Reality Card

Core Claim

The study demonstrates that IoT-based connected vehicle data can effectively identify and forecast risky driving hotspots, enabling proactive road safety interventions.

Method / Result

ARIMA achieved the lowest mean absolute error (MAE: 162.21) among the predictive models tested.

Limitations

The study's findings may be limited by the volume of training data available for deep learning models.

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