Chain conflicts would cause chain-reaction crashes, which might result in elevated fatality rates. Chain conflicts describe a phenomenon wherein evasive actions taken by a following vehicle's driver after a conflict impact nearby vehicles, which occur frequently but are reported less often. To effectively reduce conflict risk, comprehending the evolution patterns of chain conflicts under varied traffic conditions and road segments is crucial, in order to make chain conflicts management strategies. Initially, rear-end or sideswipe conflicts between two vehicles are identified based on vehicle trajectory data captured by an unmanned aerial vehicle group. Subsequently, a chain conflict identification algorithm is proposed, considering the randomness of occurrence time and fluctuation of impact duration, to link individual conflicts. Chain conflict rates exhibit significant variations across different road segments under diverse traffic conditions. Multiple risk and propagation indicators are extracted to unveil latent characteristics of chain conflicts from a high-level perspective. Based on prominent characteristic disparities, three evolution patterns are identified, i.e., Longitudinal Risk Decrease Pattern, Longitudinal Risk Increase Pattern, and Comprehensive High-risk Persistent Pattern. Spatial-temporal high-risk areas associated with each pattern are determined, and transition probabilities between patterns are calculated. The results indicate that these patterns tend to remain stable, with transitions mainly occurring from low-risk to high-risk patterns. Moreover, strategies to reduce conflict risk are proposed based on the characteristics of different patterns. This study holds great significance in understanding chain conflict evolution patterns and preventing chain-reaction crashes.
Keywords: Conflict risk quantitation; Multi segment types; Road chain conflict patterns; Traffic risk control strategies; Traffic states evolution; Vehicle trajectory data.
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