## 1 Introduction  
Traffic congestion has emerged as one of the most pressing challenges in modern urban management, significantly impacting economic productivity, environmental sustainability, and public health. The rapid rise in private vehicle ownership, particularly in densely populated countries such as China, has intensified congestion across major metropolitan areas. As of 2022, the number of registered vehicles in China exceeded 320 million, and the number of licensed drivers surpassed 452 million, resulting in severe traffic congestion during peak hours [1]. Economically, congestion contributes to substantial productivity losses--an estimated USD 160 billion annually in the United States alone, primarily due to delays in the movement of goods and services [2]. Environmentally, idling vehicles emit large quantities of greenhouse gases such as carbon dioxide (CO\({}_{2}\)) and nitrogen oxides (NO\({}_{\text{x}}\)), exacerbating urban air pollution and global warming. According to the IPCC, the transportation sector accounts for approximately 23% of global CO\({}_{2}\) emissions from fuel combustion, with road transport responsible for nearly 75% of that share [3]. From a public health perspective, prolonged exposure to vehicle emissions has been linked to increased risks of respiratory and cardiovascular diseases, as well as heightened stress levels due to noise pollution [4]. These facts underscore the urgent need for innovative and scalable solutions to optimize urban traffic flows. One of the most cost-effective and impactful strategies to mitigate urban congestion is the optimization of traffic signal control at intersections. Over the past decades, various traffic signal control strategies have been developed, ranging from simple fixed-time schedules to complex real-time adaptive systems. Fixed-time control methods, such as the Webster algorithm [5], rely on pre-configured timing plans and perform poorly under variable traffic conditions. Semi-adaptive or actuated control systems, which detect vehicle presence via sensors and adjust signal durations accordingly, offer greater flexibility [6]. Between these two, dynamic control systems like TRANSYT and TASS dynamically adjust signal phases based on predefined logic rules or traffic flow predictions [7; 8], but still often operate in a localized fashion. More sophisticated adaptive systems such as SCATS (Sydney Coordinated Adaptive Traffic System) [9] and SCOOT (Split Cycle Offset Optimization Technique) [10] respond to real-time traffic data to optimize signal plans across a network of intersections. However, these systems typically require extensive sensor infrastructure and centralized data processing, which pose significant scalability and cost challenges. Furthermore, both SCATS and SCOOT represent centralized control architectures, which do not leverage the potential benefits of decentralized or hybrid models. Notably, decentralized systems such as MOTION [11] and TASS [8] provide more distributed control policies, offering better scalability and robustness in some urban deployments. Despite the variety of available approaches, conventional traffic control methods are limited by their inability to holistically adapt to complex and dynamically changing traffic environments. They often focus on local optimization and fail to capture interdependencies between neighboring intersections, leading to inefficiencies such as increased queue lengths and poor network throughput. Additionally, these methods struggle to scale in large urban networks consisting of thousands of signalized intersections--like the city of Manhattan, which contains more than 2500 traffic lights requiring continuous coordination [12]. To address these limitations, this study proposes a Hybrid Multi-Agent Deep Reinforcement Learning (MADRL) framework that incorporates a spatio-temporal attention mechanism to facilitate intelligent, distributed traffic signal control.