Citation

Li X, Goldberg DW, Chu T, Ma A. Trans. GIS 2019; 23(3): 538-557.

Copyright

(Copyright © 2019, John Wiley and Sons)

DOI

10.1111/tgis.12540

PMID

unavailable

Abstract

Traffic collisions have been well acknowledged as a significant threat to public health, closely related to human driving errors. This study introduces an innovative approach to investigate spatiotemporal distributions of individualized driving errors and to characterize hazardous driving scenes, in which drivers are more prone to make driving mistakes. We first create a multi-feature-fusion framework to extract driving errors using smartphone sensors. Then, the detected errors are geo-statistically analyzed with road networks and driving trajectories to identify driving error hotspots. We next construct a "scenic tuple" for representing the occurrence of driving errors. Finally, the individualized hazardous driving scenes are extracted by mining a long-term collection of scenic tuples.

RESULTS demonstrate that our proposed approach can effectively identify driving errors. Additionally, the spatiotemporal patterns of driving mistakes can be identified from the individualized hazardous driving scenes, which has the potential to aid in reducing driving risks.

Language: en