Impact of Event Encoding and Dissimilarity Measures on Traffic Crash Characterization Based on Sequence of Events
This work addresses the problem of improving crash sequence analysis for traffic safety researchers and practitioners, but it is incremental as it adapts existing sequence analysis techniques to a specific domain.
The paper evaluated how different event encoding schemes and dissimilarity measures affect the clustering and characterization of traffic crash sequences, finding that a transition-rate-based localized optimal matching dissimilarity with consolidated encoding performed best, achieving the highest agreement with a benchmark categorization.
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.