SENE: A novel manifold learning approach for distracted driving analysis with spatio-temporal and driver praxeological features

Although many studies have been conducted on distracted driving, the growing number of accidents on roads demands further serious attention. Most real-world distracted driving data are unlabeled and high-dimensional, making analyses complex. There is a lack of proper indices to understand the perilousness of distracted driving, making it difficult to identify roads or neighborhoods with higher risk of accidents. Previous studies focused either on spatio-temporal or praxeological factors separately, but did not consider both together. Furthermore, crisp rule extraction and interpretation are largely missing in the literature.

To address these challenges, this study proposes a novel methodology that:

1. Develops Schrodinger Eigenmap Neighborhood Embedding (SENE) manifold learning for dimensionality reduction.
2. Incorporates both spatio-temporal and driver praxeological features.
3. Provides interpretable insights into distracted driving risk.