An Assessment of Road Conditions and Traffic Situations Impact on Multi-type Single and Chain Conflicts

Traffic conflict is the underlying state before a traffic crash occurs. Understanding the impact of static road attributes and dynamic characteristics of traffic flow on traffic crashes is crucial. However, existing research primarily focuses on the hazardous states between two vehicles, neglecting events involving multiple traffic entities. To effectively extract various types of traffic conflicts, including both single and chain conflicts, this study utilizes drone-acquired vehicle trajectory data to identify single conflicts between vehicles and subsequently identifies chain conflicts through association matching. In addition, a chain conflict can be divided into three patterns, i.e., Longitudinal Risk-Decrease Pattern, Longitudinal Risk-Increase Pattern, and Comprehensive High-Risk-Persistent Pattern. Subsequently, a nested Logit model is developed to explore the influence of macroscopic traffic attributes and road conditions on various types of single and chain conflicts. The findings reveal that merging segments and basic segments of roads are high-risk regions for single conflicts, while diverging segments and weaving segments are prone to happen chain conflicts, particularly those of comprehensive high-risk persistent pattern. Interestingly, an increase in the number of lanes helps mitigate severe chain conflicts. Additionally, as the traffic density in mainlines rises, the probability of chain conflicts increases. The volume ratio of ramp to mainline correlates positively with chain conflict occurrence in which the Longitudinal Risk Increase Pattern being the most sensitive. The traffic flow conditions under which each type of conflict occurs, combined with the analysis of macroscopic fundamental diagrams, indicate that conflict occurrences exhibit peaks. Moreover, the critical density at which conflicts are the most frequent on road segments exceeds the critical density indicated by the macroscopic fundamental diagram for the same segment. These conclusions hold substantial importance for understanding the macro causes of multi-vehicle chain conflicts, and for effectively preventing their evolution into chain collisions.