## 1 Introduction  
On main urban roads, vehicles often encounter red lights at single-point signal-controlled intersections, which results in poor traffic flow. Green wave control technology, has therefore received close attention as an important means of urban traffic management [1; 2; 3]. It determines the traffic offset, which allows vehicles to enter a coordinated road and pass through several intersections without stopping based on the design speed and spacing between intersections [4]. Although green wave control can improve traffic efficiency on arterial roads, it often fails to achieve the desired operational effect owing to various factors [5; 6], which has prompted extensive research. Most research has focused on algorithms to optimize the green wave based on two classical strategies: bandwidth maximization and cost minimization. The former is based on graph-based and numerical methods and mathematical models. For example, [PERSON] et al. [7] considered the influences of cruising speed and residual queues on traffic offset at a red light, and then optimized the classical bidirectional green wave graphic method. [PERSON] et al. [8]analyzed the shortcomings of the existing traditional numerical solution algorithm to solve the two-way green wave bandwidth problem and then improved it. [PERSON] et al. [9] proposed a method to optimize the timing of traffic lights based on deep reinforcement learning to dynamically adjust the offset. [PERSON] et al. [10] proposed a variable cycle-based method to optimize the bandwidth of the green wave and used it to optimize the green split in real time. [PERSON] et al. [11] proposed an overlapping phase-based signal-control logic and a bus priority control algorithm under two-way signal coordination on arterial roads. Methods of optimization designed to minimize the cost seek to reduce the number of stops, queue length, and delays. For example, [PERSON] et al. [12] proposed a coordinated green wave control model to optimize the distribution of adjacent intersections by minimizing delays. To minimize queue length, [PERSON] et al. [13] proposed two methods that use AI to coordinate traffic on arterial roads. The above studies investigated algorithms to optimize the green wave, but when applied to arterial roads different algorithmic models differed in adaptability [14]. Although different indicators were evaluated and verified by using traffic simulation, a unified method to assess the operational effect of the green wave on arterial roads remains elusive. As a consequence, no method is available to evaluate schemes of green wave control uniformly based on different algorithmic models. Among the researchers who have sought to develop a unified method, [PERSON] [15] evaluated the coordination-related performance of an adaptive system of signal control by using travel time and the number of stops as indicators. [PERSON] et al. [16] evaluated the advantages and disadvantages of driving-based, coordination-based, and adaptive control strategies. [PERSON] et al. [17] evaluated green wave coordination based on travel time, number of stops, and intersection delays. [PERSON] et al. [18] selected delays, queue length, stopping rate, speed, and saturation to establish a grade evaluation and ranking model of operational efficiency on arterial roads. [PERSON] et al. [14] designed an indicator to assess green wave-based coordination to evaluate different control schemes. [PERSON] et al. [19] proposed a method of assessment based on grey relational analysis by using the travel time and the single-vehicle delay of the green wave as evaluation indicators. [PERSON] [6] took the rate of use of green lights, delays, and number of stops as evaluation indicators and calculated the grey relational degree between several schemes and the ideal scheme. The above examples show that researchers have used the number of stops, travel time, delay, speed, and queue length to assess schemes of green wave control [6; 17; 18; 19] based on simulations [14] and grey relational analyses [6; 19].