## 1 Introduction
This aims to reduce intersection delays and stops to improve traffic control efficiency. [PERSON] et al. [22] associated vehicle queue length with adaptive signal control in the arterial coordinated control model and estimated queue length using connected vehicle technology. [PERSON] et al. [23] proposed a vehicle speed guidance strategy based on optimal control, using vehicle-guided speed and the phase difference of the mainline green wave to improve traffic efficiency and safety. [PERSON] et al. [15] replaced vehicle start-stop actions with vehicle acceleration and deceleration control, constructing a guidance model with the maximum vehicle speed as the control target. [PERSON] et al. [24] combined adaptive traffic signal control with vehicle trajectory construction algorithms, coordinating and optimizing vehicle trajectories and traffic signal phases to reduce congestion, travel time, and fuel consumption. [PERSON] et al. [25] established a mainline coordination control model that dynamically adjusts the green wave bandwidth based on vehicle arrival conditions, using real-time traffic flow data collected by network detection devices. This improved the coordination effect of the mainline. [PERSON] et al. [26] proposed a vehicle coordination control strategy that regulates the headway distances between platoons at intersections to ensure safe crossing for conflicting directions. They also constructed a stochastic analysis model for intelligent intersection traffic optimization control, which is only applicable to single-direction through vehicles. [PERSON] et al. [27] established a guidance strategy based on green wave speed and energy-efficient driving speed according to the real-time operating status of vehicles and signal timing conditions. [PERSON] et al. [28], based on Internet of Vehicles (IoV) technology, optimized green light timing to intelligently control intersections. They demonstrated that this method effectively reduces the number of queued vehicles. [PERSON] et al. [29] considered bidirectional traffic demand on arterial roads and proposed a coordinated control model for asymmetric traffic demand. This model combines oversaturation control and green wave control strategies to optimize the arterial capacity in the oversaturated direction, reduce vehicle delays, and increase green wave bandwidth in the undersaturated direction, effectively addressing tidal traffic congestion issues. [PERSON] et al. [30] proposed a green wave speed guidance model based on the goal of non-stop passage through intersections. This model optimizes vehicle trajectories and signal control parameters, reducing the number of stops and congestion at intersections. [PERSON] et al. [31] studied the priority passage of emergency vehicles in an intelligent connected environment and proposed a heuristic algorithm based on simu control. When an intersection faces multiple priority requests simultaneously, this algorithm can evaluate phase times and determine the optimal signal phase sequence. Although the aforementioned studies consider factors such as signal timing, speed guidance, and intersection operating status for vehicle-signal interaction optimization, they still have limitations. These include a singular focus on operating conditions, coordination, and optimization metrics limited to the main traffic flow direction and insufficient consideration of coordinated control for adjacent intersections with unequal cycles. This paper focuses on adjacent intersections with unequal cycles on urban roads. Drawing upon vehicle-road collaborative technology, it proposes a multi-strategy combined coordinated control approach. The objective is to enhance the efficiency of coordinated through traffic in the main direction. This is achieved by the dynamic adjustment of the signal phase timing and guidance of vehicle travel speeds to reduce delayed arrival, queued vehicles, and early arrivals in the through-traffic direction.