Citation

Ashifur Rahman M, Das S, Sun X. Transp. Res. Rec. 2022; 2676(8): 159-173.

Copyright

(Copyright © 2022, Transportation Research Board, National Research Council, National Academy of Sciences USA, Publisher SAGE Publishing)

DOI

10.1177/03611981221082582

PMID

unavailable

Abstract

Rainy weather significantly affects traffic safety, especially in the State of Louisiana. With the aim of identifying the patterns of collective association of attributes in rainfall-involved crashes statewide, crashes that occurred during rainy weather and resulted in two injury groups, fatal and severe injury (FSI) and moderate injury (MI), were extracted from databases acquired from the Louisiana Department of Transportation and Development. A total of 3,381 crashes were extracted, comprising 502 FSI crashes (14.85%) and 2,879 MI crashes (85.15%). This study applied the cluster correspondence analysis (CCA) method, a unique method in combination with cluster analysis and correspondence analysis, to generate clusters by partitioning of individual attributes based on the profiles over the categorical variables identified through dimensional reduction of the dataset. In addition to the biplots illustrating the association of all attributes in the clusters, the top 20 standardized residuals indicating the stronger association are presented in bar plots. Four optimum clusters from FSI and MI crashes reveal that the association of roadway, crash environment, and driver condition characteristics identified in the clusters are highly distinguishable across roadway functional classes. Specifically, varieties of attributes linked to speed limit, lighting condition, alignment, area type, manner of collision, restraint usage, and alcohol/drug can have associative impacts on these two injury severities. The identified associations of crash attributes across various functional class roadways could provide valuable understanding for the development of countermeasures which prioritize the prevention of fatalities and injuries.

Language: en

Keywords

cluster correspondence analysis; rainy weather crashes; road weather; unsupervised algorithm; weather related crashes