###### Abstract  
Accurate identification and analysis of traffic conflicts through surrogate safety measures (SSMs) are crucial for safety evaluation in road systems. Existing SSMs for conflict identification and analysis mostly consider the temporal-spatial proximity of conflicts without taking into account the severity of potential collisions. This makes SSMs unsuitable for traffic safety evaluation in complex road environments. In order to address the shortcomings above, this study first introduces a new SSM called the Potential Conflict Risk Index (PCRI). To validate the effectiveness of PCRI, the inD dataset is adopted for conflict identification comparison between time-to-collision (TTC) and PCRI. Using PCRI, this study conducts a conflict analysis in the freeway merging areas based on the data from the Outer Ring Expressway Dataset (ORED), accounting for differences between cars and trucks. The comparative results between TTC and PCRI show that PCRI can provide a more comprehensive identification of conflicts and a more accurate identification of the moment with the highest conflict risk. The results of conflict analysis suggest that conflicts occur more frequently in situations involving trucks, and these conflicts commonly occur in closer proximity to the on-ramp at freeway merging areas. The findings from this study can improve the accuracy of conflict identification under different conflict patterns, enhancing the specificity of traffic safety measures and ultimately ensuring the safety of road systems. surrogate safety measures; conflict risk; temporal-spatial proximity; severity of potential collisions; freeway merging area; vehicle types +
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methods for traffic safety evaluation are methods based on statistical models and methods based on conflicts [7]. Traditional statistical models focus on the analysis of crash frequency and draw upon extensive historical data on accidents as foundational evidence [8; 9]. However, due to the infrequent occurrence of accidents, the data available for analysis might be constrained in specific situations [10]. Based on the heightened occurrence of conflicts near collision sites, conflict-based methods have garnered widespread attention. As a category of traffic safety evaluation methods based on conflict analysis, SSMs have emerged as a prominent alternative for evaluating traffic safety. Existing SSMs can be divided into two primary categories: those based on evasive actions and those based on proximity [11]. In the definition of evasive action-based measures, conflicts have been associated with such occurrences as the appearance of brake lights or the unexpected changing of lanes or direction [12]. Several identified evasive actions, such as braking or lane-changing, are often precautionary measures rather than indicators of imminent danger for collisions [13; 14; 15]. Consequently, a strong correlation between collisions and conflicts might not be established if conflicts are only defined based on observed evasive actions. Moreover, in scenarios where vehicles in two incidents share similar driving states but differ significantly in vehicle proximity, they could be assigned the same level of conflict risk based on the assessment of evasive actions taken. This approach could result in scenarios where vehicles, being far apart and unlikely to collide, are nonetheless categorized as possessing a high conflict risk [11]. A fundamental strength of proximity-based measures resides in the assumption that every collision is preceded by a conflict. This results in a strong correlation between collisions and conflicts. Various proximity measures have been developed to quantify the temporal or spatial proximity of collisions for interactions among road users. The most commonly employed measure is time-to-collision (TTC) [16]. [PERSON] first proposed TTC, which is defined as the time remaining before a collision occurs between a leading and a following vehicle if two vehicles maintain their speed at that moment [17].