# Traffic Flow Prediction Method Based on Seasonal Characteristics and SARIMA-NAR Model
###### Abstract  
Traffic flow is used as an essential indicator to measure the performance of the road network and a pivotal basis for road classification. However, the combined prediction model of traffic flow based on seasonal characteristics has been given little attention at present. Because the seasonal autoregressive integrated moving average model (SARIMA) has superior linear fitting characteristics, it is often used to process seasonal time series. In contrast, the non-autoregressive dynamic neural network (NAR) has a vital memory function and nonlinear interpretation capabilities. They are suitable for constructing combined forecasting models. The traffic flow time series of a highway in southwest China is taken as the research object in this paper. Combining the SARIMA (0,1,2) (0,1,2)\({}_{12}\) model and the NAR model with 15 hidden layer neurons and fourth-order delay, two combined models are constructed: the linear and nonlinear component combination method is realized by the SARIMA-NAR combination model 1, and the MSE weight combination method is used by the SARIMA-NAR combination model 2. We calculated that the prediction accuracy of SARIMA-NAR combined model 1 is as high as 0.92, and the prediction accuracy of SARIMA-NAR combined model 2 is 0.90. In addition, the traffic flow forecast under the influence of the epidemic is also discussed. Through a comprehensive comparison of multiple indicators, the results show that the SARIMA-NAR combined model 1 has better road traffic flow fitting and prediction effects and is suitable for the greater volatility of traffic flow during the epidemic. This model improves the effectiveness and reliability of traffic flow forecasting, and the forecasting process is more convenient and efficient. s 2022 202218 February 202218 February 202  
(ARIMA) [13, 14], and support vector regression [15, 16]. Among these methods, ARIMA is widely used because it performs best in linear fitting and can make full use of the historical data of the series [17]. In 1979, [PERSON] et al. used the ARIMA model to predict traffic flow and occupancy of the highway [18]. The neural network has a strong ability to deal with the nonlinear problem. In 1999, [PERSON] proposed the method of using the artificial neural network to predict road conditions [19]. However, a single prediction model cannot accurately capture the complex information of traffic flow time series. Based on this, many scholars have carried out research on combined prediction models. According to the combination principle, the main combination methods can be divided into the following types: method of combining linear and nonlinear component, method of assigning weight combinations, method of stacking combination after decomposition, and the space-time combination method, etc. The specific methods are shown in Table 1. It is known that the combination model can more effectively extract the information in the time series of traffic flow. However, some scholars have tried to consider seasonal effects in the existing traffic flow forecasting research work, but no one has adopted the combined model of the seasonal autoregressive integrated moving average model (SARIMA) and the non-autoregressive dynamic neural network (NAR). SARIMA can not only deal with non-stationary data, but also accurately capture the characteristics of seasonal data with trends and cycles to make up for the lack of research on the change laws and characteristics of meso-level traffic flow [20, 21]. At the same time, traffic flow data have a significant random fluctuation due to sudden and major events. NAR can consider and effectively extract the nonlinear fluctuation components existing in the sequence. Static neural networks, such as the BP neural network, which are common in traffic flow prediction, have no memory function and rely only on the current input, so their prediction ability is far lower than that of dynamic neural networks [22]. This paper takes a highway in southwest China as the research object.