Predicting hourly traffic volume of urban signal intersections using Dynamically Weighted LightGBM

Predicting traffic volume of urban intersections is critical to ensure the stability and efficiency of urban road network. To better capture spatio-temporal nonlinear correlation between traffic volume and contributing factors and improve the prediction performance, this study introduces a Dynamically Weighted LightGBM (Gradient Boosting Machine) framework (DW-LGBM) to forcast the hourly intersection traffic volume. The improvement of the DW-LGBM includes three core components: a dynamic weight allocation component that captures nonlinear spatio-temporal dependency, a multi-dimensional feature engineering component that incorporates cyclical temporal trend, and a dual-stage noise suppression mechanism using Exponentially Weighted Moving Average (EWMA) and Kalman filtering to smooth the data. The proposed model is trained and tested with hourly traffic volume collected from 209 urban intersections during 31 days in Chengdu China. The results show that the predictions achieve superior performance metrics which surpass those of the baseline models (e.g., LSTM and XGBoost). The proposed architecture exhibits exceptional spatio-temporal adaptability for different urban intersections. However, it is found that all the models perform woeful in predicting traffic volume during peak hours due to the significant heterogeneity among intersections.