Adding location and time data to driving sensor inputs significantly improves driving style classification accuracy by 13%, showing that contextual geo-information is crucial for understanding driver behavior beyond raw acceleration/speed metrics.
This paper presents a low-cost system that uses two physical sensors and a neural network to automatically recognize and classify driving styles (normal, aggressive, etc.) in real-world vehicles.