Research on short-term traffic flow prediction based on SARIMA model

Jingyi Wang; Li He; Xichun Zhang; Wei Liu

doi:10.1117/12.2623999

6 February 2022 Research on short-term traffic flow prediction based on SARIMA model

Jingyi Wang, Li He, Xichun Zhang, Wei Liu

Proceedings Volume 12081, Sixth International Conference on Electromechanical Control Technology and Transportation (ICECTT 2021); 1208132 (2022) https://doi.org/10.1117/12.2623999
Event: Sixth International Conference on Electromechanical Control Technology and Transportation (ICECTT 2021), 2021, Chongqing, China

Abstract

In order to improve the accuracy of traffic flow prediction and provide more reasonable decision-making basis for traffic trips, seasonal difference autoregressive moving average (SARIMA) model is selected to establish a traffic flow prediction model, and jupyter platform is used to analyze the traffic flow data of the first hour of each period in July and August 2019 in Xuancheng City, Anhui Province. Stabilizing and preprocessing the time series data of traffic flow to eliminate its trend and seasonal factors; based on the stationary time series, the SARIMA model is established, and its parameters are estimated and the model order is determined, and the optimal model SARIMA(p, d, q)(P, D, Q)s is obtained. The model is used to predict the traffic flow in the next hour, and the error test is carried out on the results. The test results with small error show that SARIMA model is practical in forecasting traffic flow

Citation Download Citation

Jingyi Wang, Li He, Xichun Zhang, and Wei Liu "Research on short-term traffic flow prediction based on SARIMA model", Proc. SPIE 12081, Sixth International Conference on Electromechanical Control Technology and Transportation (ICECTT 2021), 1208132 (6 February 2022); https://doi.org/10.1117/12.2623999

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