Analyzing Urban Midblock Crash Severity Outcomes Using Proposed Three-Step Pattern Clustering
Publication: Journal of Transportation Engineering, Part A: Systems
Volume 149, Issue 7
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 -mode clustering algorithm. This provides a clear set of important nonoverlapping crash severity patterns. Important crash severity patterns for crashes occurring at midblock sections of low-speed urban roads are analyzed using the proposed algorithm. It was observed that heavy vehicle or vulnerable road user involvement play an important role in deciding severity outcomes. Important influence of illegal median cut, shoulder, and marking on crash severity outcomes could be observed.
Practical Applications
Researchers have proposed modified and staged pattern mining to increase efficiency of obtaining significant patterns. However, many overlapping patterns generated by these methods make filtering of significant patterns difficult. This work proposes a three-stage pattern mining approach that can mine few important nonoverlapping significant patterns sufficient for understanding the process. The proposed approach is illustrated with data for urban road network to understand crash occurrence and severity outcome patterns. Various major insights could be drawn that provide valuable input to safety experts and practitioners for safety improvement of such roads. It was observed that shoulder condition and pavement distress condition play important role in deciding safety. Proper maintenance of shoulder and pavements is essential for safety improvement. Also, illegal median breaks at midblock segments, providing illegal crossing points for vulnerable road users, often witnessed major or fatal crashes. Thus, crosswalks need to be provided at regular intervals. The presence of proper road markings also reduced crash severity. Controlled intersections proved to be safer and when the controls were relaxed during night time, the intersections were observed to witness severe crashes. Also, the proposed three-stage pattern mining method may be used in any field to understand process patterns.
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Data Availability Statement
Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.
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Published In
Journal of Transportation Engineering, Part A: Systems
Volume 149 • Issue 7 • July 2023
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© 2023 American Society of Civil Engineers.
History
Received: Aug 6, 2022
Accepted: Feb 23, 2023
Published online: May 10, 2023
Published in print: Jul 1, 2023
Discussion open until: Oct 10, 2023
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