# Integrating Autonomous Vehicles (AVs) into Urban Traffic: Simulating Driving and Signal Control
## 1 Introduction  
In the ever-evolving landscape of urban transportation, the integration of autonomous vehicles (AVs) stands as a pivotal advancement that holds the promise of reshaping how we navigate our cities [1, 2]. Autonomous driving technology has garnered considerable attention for its potential to revolutionize traffic dynamics, enhance road safety, and contribute to sustainable urban living [3, 4]. As cities globally explore the possibilities of incorporating AVs into their traffic ecosystems, a critical point of intersection arises at signalized intersections essential nodes in urban traffic management [5, 6]. Among the various driving behaviors associated with AVs, platooning has emerged as a particularly promising approach. Vehicle platooning involves a group of AVs traveling in coordinated formations, maintaining short gaps between one another through real-time communication and synchronization. The key benefits of platooning include reduced aerodynamic drag, which improves fuel efficiency, and more coordinated vehicle movements that enhance road capacity and traffic flow [7]. By reducing inter-vehicle gaps, platoons can minimize the stop-and-go behavior common in mixed traffic environments, leading to smoother traffic flow and reduced congestion, especially at intersections. Additionally, vehicle platooning contributes to lower emissions due to more efficient driving patterns, making it a key strategy for promoting sustainable urban mobility. AVs are heralded as the vanguard of a transformative era in urban mobility, promising to revolutionize transportation systems and redefine the relationship between humans and their vehicles [8]. However, the integration of AVs into existing traffic systems presents unique challenges, particularly in the context of signalized intersections. Previous studies have explored various aspects of AV integration, including their impact on traffic flow stability and throughput on highways [1; 2], and the influence of dedicated lanes for connected and autonomous vehicles [3]. Despite these advancements, there remains a significant gap in understanding the effects of AV driving behaviors at urban intersections. Cautious AVs prioritize safety and adherence to traffic rules [7; 8; 9]. Mimicking conservative human driving styles, they are expected to exhibit a restrained approach, avoiding abrupt maneuvers, and ensuring a conservative interaction with the traffic environment. The risk-averse nature of cautious AVs is expected to lead to increased queue lengths and travel times, especially under high-penetration scenarios. Normal AVs, representing a balance between safety and efficiency [10], are anticipated to emulate standard human driving patterns. Their behavior is expected to closely mirror the traffic dynamics observed in a conventional, human-driven scenario. At moderate penetration rates, their influence on key traffic parameters may maintain a neutral impact, aligning closely with the natural flow of traffic. In contrast, aggressive AVs are engineered to prioritize efficiency [11], often characterized by dynamic driving and optimized traffic flow. It is anticipated that their assertive maneuvers and quick decision-making may result in decreased queue lengths, travel times, and delays. However, the potential trade-off could involve an increase in emissions and fuel consumption. Platoon-forming AVs operate in cohesive groups, synchronized for optimal traffic flow [12; 13]. Through leveraging communication and coordination, these vehicles aim to reduce gaps between each other, potentially minimizing congestion and improving overall intersection efficiency. Despite these advancements, there remains a lack of comprehensive studies that address how different AV driving behaviors impact the performance of urban intersections under various traffic signal cycle times. This study aims to fill this gap by investigating the impact of cautious, normal, aggressive, and platoon-forming AV behaviors on traffic performance at a four-leg signalized intersection with heavy traffic volume.