## 1 Introduction
### Literature Review  
The first traffic model to analyze bottleneck behavior was introduced in [16]. This model was extended in [17] by incorporating schedule delay and travel time costs. A more general approach was employed in [18] to analyze congestion at bottlenecks considering two types of drivers. However, these early models are simplistic and cannot effectively characterize traffic behavior at bottlenecks [7]. The impact of tolls on congestion management at bottlenecks was examined in [19]. However, a constant arrival time was considered for all vehicles [7]. The model in [16] was employed in [20] to investigate the effects of autonomous vehicles during congestion. A VSL control method was developed in [21] to reduce the risk of rear-end collisions at bottlenecks. It was evaluated using a platoon of connected vehicles. Connected vehicle technology was employed in [22] to improve bottleneck throughput and reduce traffic delays. However, the results do not reflect real-world conditions [4]. An optimal lane management policy was developed in [23] for CAVs and human-driven vehicles on highways. Results were obtained considering the car-following mode, headway, and platoon size, which show that dedicated CAV lanes can improve throughput and traffic efficiency. CAV lane-changing behavior in mixed traffic conditions was studied in [24] via dynamic programming and model predictive control. A platoon-based control strategy for CAVs was proposed in [25] to improve traffic efficiency and reduce congestion. A model was developed in [26] to examine how CAV platoon characteristics can be exploited to optimize traffic flow efficiency. The model in [27] was developed to analyze how CAV market penetration rates impact traffic flow and congestion. Both microscopic and macroscopic traffic models have been developed. Microscopic models characterize individual vehicle behavior and employ parameters such as position, velocity, time, and distance headway [28; 29]. Conversely, macroscopic models describe collective behavior such as average density and velocity [30]. A microscopic model was developed in [31] to evaluate vehicle behavior in dense traffic conditions. It was shown that distance headway affects traffic flow as a large distance headway allows vehicles to move at higher velocities, thereby reducing density. However, the acceleration at high speeds is large, which is not realistic [32]. This model was modified in [33] by incorporating the difference between actual and safe velocity. However, the acceleration and deceleration are unrealistic compared to real-world data [34]. A model was proposed in [35] that considers sensitivity and driver response to traffic conditions, but stability is an issue. A model based on driver response was proposed in [36]. However, it assumes all drivers have the same reaction time [34]. The model in [37] employs a variable reaction time. It produces stable traffic behavior for small reaction times but large values can result in collisions [34]. The intelligent driver (ID) model was developed in [32] to overcome the drawbacks of previous models. It can provide realistic acceleration and deceleration and collision-free behavior [34]. However, this model ignores driver reaction time and characterizes traffic based on a constant exponent which is not related to traffic physics and so produces unrealistic results [34; 38]. A model incorporating reaction time and a variable exponent was proposed in [39]. However, it does not consider CAVs. The ID model has been used to investigate the behavior and stability of CAVs [40; 41] and to evaluate the influence of CAVs on road capacity [42]. However, it cannot produce CAV behavior in real-traffic conditions [43]. A flowchart of the methodology employed in this paper is given in Figure 1. An appropriate location in Peshawar, Pakistan was selected to install a sensor node to collect traffic data from the roadside. This data was analyzed to obtain traffic parameters.