## 1 Introduction  
With the rapid development of the global economy, car ownership and people's travel demands have gradually increased, thus bringing huge challenges to urban traffic [1]. To alleviate urban traffic congestion and reduce the hazards caused by traffic congestion, traffic managers have proposed the following solutions: (1) Expansion of existing urban roads and other infrastructure, thereby increasing the capacity of the roads. However, in urban planning, the limited area of land use makes it difficult to build urban infrastructure to meet traffic demands, and some studies have shown that road expansion only relieves traffic congestion in the short term [2]; however, according to the theory of supply and demand, the potential traffic demand will increase, leading to a more serious traffic congestion problem [3]. (2) Establish an intelligent transportation system (ITS) that applies information, communication, sensor, and computer technologies to road traffic guidance and management. Road managers dynamically adjust control methods through real-time traffic status to improve road management [4], and ITS can provide road congestion information for travelers to choose a reasonable route out and improve travel efficiency, so it is vital for relieving urban traffic congestion ITS, and traffic flow prediction is the key for ITS to achieve active services, which can provide accurate real-time traffic information for ITS. Traffic flow prediction refers to the prediction of traffic flow in a future period by historical traffic flow information, from the initial statistical learning models, nonlinear theoretical methods, and shallow neural network models to the current large-scale use of deep learning methods and statistical models through the periodic patterns in historical data for prediction; the theory is simple, but it is difficult to deal with the dynamic changes in traffic flow data. Shallow neural networks have limitations in the face of larger data samples [5], and the traffic system is highly susceptible to interference from the external environment, resulting in a large number of nonlinear features in the traffic flow [6], and a single model cannot handle both linear and nonlinear features in the traffic flow, so a combined prediction model is necessary to improve the prediction accuracy. Traffic flow data are not only correlated in time, with a certain regularity in continuous periods, but also correlated in space due to the topology of the traffic network, and the current cross-section traffic flow is influenced by the upstream and downstream traffic states. The model is easier to capture the potential features of the unsteady traffic flow data by decomposing the data to obtain multiple smoother components. For this reason, [PERSON] et al. [7] define that a good traffic flow prediction method should have the following characteristics: (1) ensure that the model completely extracts the salient features in the traffic flow, (2) be able to consider the spatio-temporal correlation of traffic flow in the road network, and (3) analyze the influencing factors of traffic changes. However, in the past studies on short-term traffic flow prediction, some studies considered the spatio-temporal correlation between external environmental factors and traffic flow without analyzing the potential features in traffic flow; some studies analyzed from the perspective of traffic flow decomposition, but ignored the connection between traffic flow features, while this paper is dedicated to combining the two, using the decomposition method to reduce the unsteadiness of traffic flow and fully considering the spatio-temporal characteristics of traffic flow based on this, we propose two-level decomposition-reinforcement learning optimized combined short-time traffic flow prediction model considering multiple factors. Firstly, the model has two-stage decomposition by STL and VMD, and scientific reconstruction of each component using fuzzy entropy and the Lempel-Ziv complexity index to improve operational efficiency.