Extreme gradient boosting algorithm based urban daily traffic index prediction model: a case study of Beijing, China

Abstract

The exhaust emissions and frequent traffic incidents caused by traffic congestion have affected the operation and development of urban transport systems. Monitoring and accurately forecasting urban traffic operation is a critical task to formulate pertinent strategies to alleviate traffic congestion. Compared with traditional short-time traffic prediction, this study proposes a machine learning algorithm-based traffic forecasting model for daily-level peak hour traffic operation status prediction by using abundant historical data of urban traffic performance index (TPI). The study also constructed a multi-dimensional influencing factor set to further investigate the relationship between different factors on the quality of road network operation, including day of week, time period, public holiday, car usage restriction policy, special events, etc. Based on long-term historical TPI data, this research proposed a daily dimensional road network TPI prediction model by using an extreme gradient boosting algorithm (XGBoost). The model validation results show that the model prediction accuracy can reach higher than 90%. Compared with other prediction models, including Bayesian Ridge, Linear Regression, ElatsicNet, SVR, the XGBoost model has a better performance, and proves its superiority in large high-dimensional data sets. The daily dimensional prediction model proposed in this paper has an important application value for predicting traffic status and improving the operation quality of urban road networks.

Graphical abstract

Keywords

Traffic prediction / Traffic performance index (TPI) / Influencing factor / XGBOOST / Machine learning model