## 2 Background and Related Work  
### Key Challenges in Urban Traffic Management  
Urban traffic management faces numerous challenges, including congestion, unpredictable delays, and safety risks. As cities grow and vehicle numbers increase, traffic congestion becomes a major concern, especially during peak hours. Road networks, particularly at intersections, become saturated, creating bottlenecks that hinder traffic flow. Accidents, vehicle breakdowns, or road work can exacerbate congestion and cause extended delays. Poorly coordinated traffic signals, often outdated, contribute to unnecessary stops, further increasing congestion [9]. Traditional approaches like road expansion and static signals have significant limitations in handling modern urban traffic demands. These solutions are reactive, lack flexibility, and fail to address long-term sustainability and safety needs. Emerging technologies, such as adaptive traffic control, connected vehicles, and intelligent communication systems, offer promising solutions. To address these challenges, solutions have been proposed that leverage machine learning algorithms to predict optimal routes, reduce average waiting times, alleviate congestion, minimize travel costs, and lower air pollution levels. These systems analyze traffic patterns, vehicle classifications, accident data, and environmental factors, such as precipitation levels [10]. By accurately analyzing traffic demand, future conditions can be predicted and used to optimize vehicle routes. This approach becomes particularly effective in the context of autonomous vehicles, which exhibit more predictable behavior. Such predictability allows city management systems to mitigate congestion and pollution more effectively, thereby improving traffic flow through centralized control mechanisms [11]. Current traffic control algorithms often focus on optimizing vehicular flow but overlook the complexities of pedestrian movements [12]. This gap highlights the need for reinforcement-learning-based traffic systems that incorporate pedestrian safety. Traffic delays are closely tied to congestion, as static, non-adaptive traffic signals fail to respond to real-time traffic conditions. During rush hours, the road capacity is often exceeded, creating long wait times and slow travel speeds. A critical issue is the lack of communication between adjacent intersections, which hinders efficient synchronization of traffic signals, leading to delays. In multi-intersection scenarios, the scalability of single-agent systems is limited, necessitating collaborative strategies [13; 14; 15]. Adaptive traffic control in Vehicle-to-Everything (V2X) environments offers a solution by using detailed data for improved traffic flow and safety. This approach models traffic flow dynamics and enhances scalability by integrating data, such as vehicle position, speed, and queuing lengths, that traditional systems fail to capture. Compared with the traffic flow and occupancy information provided by the fixed coil detector in the traditional traffic environment, the adaptive traffic control system in the V2X environment can collect more detailed data, such as vehicle position, speed, queuing length, and stopping time. While Vehicle-to-Vehicles (V2V) links are particularly important for safety functionalities, such as pre-crash sensing and forward collision warning, Infrastructure-to-Vehicles/Pedestrians (I2V/P) links provide the CV and the pedestrians with a variety of useful information. Emerging solutions like adaptive traffic signal control (ATSC) [16], connected vehicles (CV) [17], and reinforcement learning (RL) algorithms [18] enable dynamic, real-time traffic management. These methods adapt to changing traffic patterns, reducing delays while improving safety and efficiency. For example, ATSC systems optimize traffic signal phases using sensor data, minimizing congestion. In environments with connected and autonomous vehicles (CAVs), technologies like vehicle speed guidance systems further reduce queues and delays.