Traffic Flow Prediction Method Based on Seasonal Characteristics and SARIMA-NAR Model
Source abstract
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.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Traffic Prediction and Management Techniques · Traffic control and management · Transportation Planning and Optimization
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Civil Engineering, Central South University, Changsha 410075, China
Funding
National Natural Science Foundation of China
References (48)
Show source-provided references
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