Prediction of High-Speed Traffic Flow around City Based on BO-XGBoost Model

Abstract

The prediction of high-speed traffic flow around the city is affected by multiple factors, which have certain particularity and difficulty. This study devised an asymmetric Bayesian optimization extreme gradient boosting (BO-XGBoost) model based on Bayesian optimization for the spatiotemporal and multigranularity prediction of high-speed traffic flow around a city. First, a traffic flow dataset for a ring expressway was constructed, and the data features were processed based on the original data. The data were then visualized, and their spatiotemporal distribution exhibited characteristics such as randomness, continuity, periodicity, and rising fluctuations. Secondly, a feature matrix was constructed monthly for the dataset, and the BO-XGBoost model was used for traffic flow prediction. The proposed model BO-XGBoost was compared with the symmetric model bidirectional long short-term memory and integrated models (random forest, extreme gradient boosting, and categorical boosting) that directly input temporal data. The R-squared (R2) of the BO XGBoost model for predicting TF and PCU reached 0.90 and 0.87, respectively, with an average absolute percentage error of 2.88% and 3.12%, respectively. Thus, the proposed model achieved an accurate prediction of high-speed traffic flow around the province, providing a theoretical basis and data support for the development of central-city planning.

Keywords

• traffic flow prediction
• spatiotemporal characteristics
• XGBoost algorithm
• bayesian optimization
• Bi-LSTM