Citation

Barman S, Bandyopadhyaya R. J. Transp. Eng. A: Systems 2023; 149(7): e05023003.

Copyright

(Copyright © 2023, American Society of Civil Engineers)

DOI

10.1061/JTEPBS.TEENG-7641

PMID

unavailable

Abstract

Low-speed urban midblock sections of old cities of developing countries that have multiple access point, illegal median cuts or breaks, or restricted and broken shoulders witness good number of severe and fatal crashes, comparable to crashes at intersections. Limited studies have focused on analyzing factors influencing fatal and severe crashes for these types of locations. Traditionally, crash severity patterns are analyzed by a two-step pattern mining approach. In the first step the heterogeneous crash data set is partitioned into more homogeneous groups, and important crash severity patterns are mined using the association algorithm for each group. However, large number of association rules are obtained, which are overlapping, and filtering nonoverlapping rules is a challenge. In this study a three-step crash pattern mining approach is proposed, where after obtaining the association rules, important nonoverlapping rules are filtered using the

Keywords

Association rule mining; Crash data; Crash severity; K-mode clustering; Three-step pattern mining