The development of behavior-based traffic conflict indicators through automated traffic safety analysis

Traffic collisions are a severe epidemic that causes the loss of 1.25 million lives worldwide every year, the majority of which are in developing and emerging countries. Traditionally, road safety analysis has been conducted by relying on collision records as the primary source of data. This reactive approach has several shortcomings such as the poor quality of collision data, the long observation periods, the subjectivity of evaluation, and the difficulty in understanding the mechanisms that lead to collisions. These limitations have led to the growing interest in using surrogate safety measures, such as traffic conflicts (i.e., near misses), as a proactive approach to analyzing safety from a broader perspective than collision data alone. The analysis of traffic conflicts is typically performed using a number of conflict severity measures such as Time-To-Collision and Post-Encroachment-Time. These measures rely on road-users getting within specific spatial and temporal proximity from each other and, therefore, assume that proximity is the indicator of conflict severity. However, this assumption may not be valid in all driving cultures where road-users are less organized and traffic rules are weakly enforced. In these environments, close interactions between road-users are very common and sudden evasive actions are the primary collision-avoidance mechanism. The objective of this research is to investigate the applicability of existing time-proximity measures in less-organized traffic environments and to propose evasive action-based conflict indicators as complementary measures of conflict severity. The mechanisms by which road-users perform evasive actions are studied and used to recommend new behavior-based conflict indicators. Time-proximity and evasive action conflict indicators are then compared to evaluate conflict severity at locations from five major cities with different traffic environments; Shanghai, New Delhi, New York, Doha, and Vancouver. Ordered-response models were utilized to relate both indicators to conflict severity, taking into account the unobserved heterogeneity in conflicts. The findings reveal that evasive action-based indicators are most effective in less-organized traffic environments such as Shanghai and New Delhi, with less potential in more structured environments such as Vancouver, where time-proximity measures are more effective. The results emphasize the need to select the proper conflict indicators depending on the studied traffic environment.