## 1 Introduction  
With the development of the social economy, science and technology, and the acceleration of urbanization, the number of automobiles has increased rapidly. The resulting problems of traffic congestion and right-of-way distribution are also becoming increasingly obvious and seriously affect traffic safety and efficiency. The construction of an intelligent transportation system (ITS) can effectively alleviate road congestion, shorten travel time, reduce pollution, and improve traffic safety. Accurate prediction of short-term traffic flow is the core issue of ITS, which can provide monitoring and technical support of traffic flow in a certain period in the future. Timely and accurate prediction of traffic flow is the basis and prerequisite for traffic management and travel route planning. This can help managers to take appropriate preventive measures to make travelers choose more suitable routes, thus reducing congestion on roads and improving the efficiency of distribution between roads. Among the various applications of ITS, traffic flow prediction has attracted much attention in recent decades. However, this remains a difficult topic for transportation researchers. Traffic flow forecasting is divided into short-term traffic flow forecasting and medium- and long-term traffic flow forecasting, depending on the time interval. The medium-term and long-term forecast units are generally based on days, weeks, months, and years. Due to the large interval, the data stability is relatively good, so it is often used for forecasting. The short-term traffic flow forecast is generally at intervals of 5-15 min. Due to the short time interval, the data stability is relatively poor, the complexity is high, and the random variation is large, which increases the difficulty of the forecasting work. Given the increasingly complex traffic situation, the development of more accurate short-term traffic flow forecasting to achieve accurate real-time traffic information determination is still an urgent problem to be solved. Domestic and foreign scholars have carried out extensive research on short-term traffic flow prediction. According to the research content, they can be divided into three categories: statistical theory and methods, superficial machine learning models, and deep learning models. The statistical-theory-based methods are mainly represented by the auto-regressive integrated moving average (ARIMA) model and its improved model. [PERSON] et al. [1] proposed a real-time adaptive prediction of short-term flow based on the ARIMA model. The parameters are estimated by the forgetting factor recursion, and then the linear minimum variance is used to predict the traffic flow. [PERSON] et al. [2] and [PERSON], [PERSON] et al. [3] proposed a seasonal ARIMA according to the periodicity of traffic flow to improve prediction accuracy. This kind of parametric model, based on statistical methods, has a simple structure, fast working speed, and good interpretation ability and can achieve better results with fewer data. The disadvantages are that the form is relatively rigid, it is difficult to explain complex problems, and the deviation is relatively large. To compensate for the above shortcomings, shallow machine learning models have been proposed based on the analysis of randomness and nonlinear characteristics of traffic flow, such as support vector regression (SVR) and the K-nearest neighbor model (KNN) ([PERSON] et al., 2018 [4]; [PERSON] et al., 2021 [5]). [PERSON] et al. [6] applied the support vector machine model to travel time prediction and proved the feasibility of its application. [PERSON] et al. [7] proposed a data-driven approach for short-term traffic forecasting. Such models are flexible and powerful and apply well to solving complex traffic time series problems. However, the models are too complex, more parameters need to be learned, and the training time is too long.