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

Abstract

Although there are many studies conducted on distracted driving, the growing number of accidents on roads demands further serious attention. The majority of the distracted driving-related data in real life are unlabeled and higher dimensional, leading to complex analyses. There is a lack of existence of proper indices for understanding the perilousness due to distracted driving, which makes it very difficult to understand which road or neighborhood has a higher risk of accidents. Despite earlier studies have focused on either spatiotemporal or praxeological factors separately, they have not considered both factors together. Moreover, crisp rule extraction and interpretation are lacking in the literature. Therefore, to deal with such issues, we have proposed a new methodology which: (i) develops Schrodinger Eigenmap Neighborhood Embedding (SENE) manifold learning for dimensionality reduction, followed by K-means clustering, (ii) develops road perilousness index (RPI) and neighborhood PI (NPI) to explain dangerousness of roads or neighborhoods, (iii) uses both spatiotemporal and driver praxeological factors, and (iv) develops Tolerance Rough Set Approach (TRSA) for crisp rules generation and interpretation. Road accident data from the Nevada Department of Transportation is used as a case study. Besides, a total of four benchmark datasets from the University of California Irvine repository are also used for comparative study to prove the superiority of our proposed methodology over some state-of-the-art. Experimental results reveal that the proposed methodology outperforms others by providing the highest clustering accuracy with four clusters. Finally, a set of 16 crisp rules are extracted and interpreted from clusters using TRSA.