## 1 Introduction  
Transit priority is typically reflected through two methods: dedicated bus lanes [1] and transit signal priority (TSP). Intelligent traffic technology has increasingly been utilized for TSP. Previous studies employed automatic vehicle location (AVL) systems to determine the bus's position and calculate its headway, fulfilling the requirements of TSP [2]. Currently, advanced self-driving technology has been implemented in public transportation. As illustrated in Figure 1, the vehicle's on-board unit (OBU) equipped with wireless communication technology (Vehicle to Everything, V2X) obtains real-time vehicle position and movement data at intersections. This capability enables the self-driving bus to dynamically exchange information with the road infrastructure and other vehicles, base stations, and signal lights in the vicinity. By employing holographic environmental perception [3] and real-time road-traffic data acquisition [4], the system can effectively optimize signal timing. TSP encompasses three types: passive priority, active priority, and real-time priority. The approaches used for optimizing passive priority and active priority are not well-suited for TSP at arterial intersections [5]. Previous studies on active TSP at arterial intersections predominantly focused on achieving maximum \"green wave\" and minimizing delay [6]. Conflict between the arterial coordination signal control system and the TSP control system is common [7]. The arterial coordination TSP cannot ensure the continuous normal operation of the original green wave belt, leading to potential delays and TSP failures. Utilizing the bus pre-signal method and queuing theory, the model is solved to minimize delays throughout the entire arterial line [8]. [PERSON] et al. [9] investigated signalized intersection groups in two bus stations and reduced bus delays using a proposed linear programming model. However, in arterial coordinated TSP, the delay at the upstream intersection may decrease the delay of the priority phase due to the downstream red light causing new delays. To minimize vehicle delays in coordinated arterial TSP, other studies have proposed mathematical models targeting bus schedules [10], bus headway [2], and bus priority [11]. The original ATSP factors are bus stops [12], pedestrians [13], and buses [14]. This paper, based on research of the original adaptive transit signal priority, has added the consideration of private vehicles, with the aim of reducing private vehicle delays caused by transit signal priority. The ATSP method based on the conventional mathematical model cannot ensure real-time calculation performance. In this study, real-time priority is considered to be the optimization objective for TSP, thus satisfying TSP based on arterial coordination and reducing delays for private vehicles. [PERSON] et al. [15] proposed TSP using reinforcement learning and developed a reinforcement learning (RL) agent responsible for determining the optimal duration of each signal phase, reverting to the original phase when the bus passes. ATSP is a complex issue necessitating consideration of various dynamic factors. These factors encompass the routes taken, velocity, traffic conditions, and other variables relevant to buses and other vehicles. Given the continuous variability in these elements, it is imperative that algorithms exhibit swift adaptability and robust learning capabilities. The DQN algorithm, as employed here, excels at the task due to its capacity to iteratively adjust neural network parameters through interactions with the environment. This adaptative learning process allows the algorithm to acquire an optimal strategy for resolving the ATSP. Figure 1: Vehicle information architecture diagram at intersection. Furthermore, DQN integrates techniques like experience replay, facilitating the storage of prior experiences and associated rewards to update the target Q-value.