There has been a growing interest in using traffic conflicts for studying safety from a broader perspective than relying only on collision data. The analysis of traffic conflicts is typically performed through the calculation of traditional conflict severity indicators such as Time-To-Collision (TTC). These measures rely on road users getting within specific temporal and spatial proximity from each other and assume that proximity is a surrogate for conflict severity. However, this assumption may not be valid in some driving environments where close interactions between road users are common and sudden evasive actions are frequent. Therefore, it is suggested that evasive action-based conflict indicators can be considered a complimentary approach to understand and assess traffic conflicts in some less organized traffic environments. Pedestrian evasive action indicators are mainly reflected in variations of the spatio-temporal gait parameters (e.g., step frequency and step length). The objective of this paper is to compare the use of time proximity and evasive action-based conflict indicators in measuring the severity of conflicts for different traffic environments. Video data from intersections in four major cities with varying traffic environments is analyzed. The cities include: Shanghai, New York, Doha and Vancouver. The video data is analyzed using automated computer vision techniques to extract the pedestrian involved conflicts and to calculate various conflict indicators. The results showed the effectiveness of using pedestrian evasive action-based indicators in environments such as Shanghai as compared to time to collision. However, the evasive action measures didn’t show the same relevance in organized traffic environments such as Vancouver.