  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.
