# Measuring Collision Risk in Mixed Traffic Flow Under the Car-Following and Lane-Changing Behavior
## 1 Introduction  
In the rapidly advancing era of intelligent transportation, connected and autonomous vehicles (CAVs) have emerged as a crucial solution for mitigating traffic congestion [1] and enhancing road safety [2]. The introduction of this new mixed traffic paradigm has created a complex, dynamic, and boundaryless traffic flow, highlighting the growing importance of highway safety management. As a result, the assessment of highway traffic operational risks and the assurance of safety and efficiency have become critical areas of research, particularly within the transportation field. Traditional traffic safety research, which primarily relies on historical crash data to establish connections between crash frequencies or severities and contributing factors, faces several challenges: (i) Traffic accidents, accounting for less than 0.01% of all traffic incidents on highways, exhibit characteristics of low probability, high randomness, and unpredictability, making them difficult to directly observe in real-world scenarios; (ii) historical crash data cannot be utilized for real-time or ex-ante analyses [3]. A promising alternative to overcome these limitations involves the use of near-crash or traffic conflict data [4], which occur far more frequently than crashes and provide valuable insights for estimating the safety performance of entities on the road network [5]. Moreover, numerousstudies have established statistically significant associations between traffic conflicts and observed crashes [6; 7; 8]. Since the late 20 th century, Surrogate Safety Measures (SSMs) have garnered significant attention and recognition in the field of road traffic safety analysis. With ongoing advancements in traffic collision analysis technologies, new SSMs that go beyond conventional metrics of time, distance, and speed reduction have also emerged. SSMs have contributed substantially to improving transportation safety across diverse domains, including infrastructure assessment, user behavior analysis, and emerging technology evaluation [9], ultimately supporting policy and strategy development [8]. Recently, SSMs have gained particular prominence in conflict-based analyses, owing to their capability to identify the temporal or spatial proximity of road users, which facilitates the detection, evaluation, and severity assessment of conflicts or near-misses [10]. In modern research, SSMs are crucial for evaluating CAV safety during their developmental phases. Given the scarcity of historical and generalizable safety data on CAVs, microsimulation techniques that extract vehicle trajectories and identify traffic conflicts through SSMs have proven effective in addressing data limitations while ensuring robust safety assessment methodologies for emerging autonomous technologies [9]. However, most simulation environments for safety analysis, which are often restricted to single-lane scenarios, fail to consider the impact of vehicle information interaction within a networked communication context or the effects of lane-changing behavior on safety. Consequently, the SSMs proposed for mixed traffic flows, including CAVs, are predominantly applicable to car-following risks without adequately considering lateral risks. Research on CAVs has gained significant attention due to their potential to improve safety and traffic operations. However, the technology is not yet mature enough for large-scale deployment. Despite real-world trials and advancements, autonomous vehicles (AVs) are still far from widespread adoption, particularly in safety evaluation [11]. A transition phase involving mixed traffic, where human-driven vehicles (HDVs), AVs, and CAVs coexist, is expected to persist for a long time [12]. Thus, CAV studies largely rely on microscopic traffic simulations, and SSMs have been widely adopted to quantify CAV safety benefits based on these simulations [4].