Impact of Event Encoding and Dissimilarity Measures on Traffic Crash Characterization Based on Sequence of Events

TL;DR

This study evaluates how different encoding schemes and dissimilarity measures affect crash sequence clustering, highlighting the importance of domain-aware methods for accurate traffic crash characterization.

Contribution

It systematically compares encoding and dissimilarity measures for crash sequence analysis, identifying the most effective combination aligned with domain context.

Findings

Transition-rate-based dissimilarity performs best

Consolidated encoding scheme yields highest agreement with benchmark

Selection of encoding and dissimilarity measures critically influences clustering results

Abstract

Crash sequence analysis has been shown in prior studies to be useful for characterizing crashes and identifying safety countermeasures. Sequence analysis is highly domain-specific, but its various techniques have not been evaluated for adaptation to crash sequences. This paper evaluates the impact of encoding and dissimilarity measures on crash sequence analysis and clustering. Sequence data of interstate highway, single-vehicle crashes in the United States, from 2016-2018, were studied. Two encoding schemes and five optimal matching based dissimilarity measures were compared by evaluating the sequence clustering results. The five dissimilarity measures were categorized into two groups based on correlations between dissimilarity matrices. The optimal dissimilarity measure and encoding scheme were identified based on the agreements with a benchmark crash categorization. The transition-rate-based, localized optimal matching dissimilarity and consolidated encoding scheme had the highest agreement with the benchmark. Evaluation results indicate that the selection of dissimilarity measure and encoding scheme determines the results of sequence clustering and crash characterization. A dissimilarity measure that considers the relationships between events and domain context tends to perform well in crash sequence clustering. An encoding scheme that consolidates similar events naturally takes domain context into consideration.