XGBoost–LSTM Regional Traffic Congestion Ratio Prediction Integrating Spatio‐Temporal and Weather Features

ABSTRACT Urban traffic prediction is of great significance for traffic management and optimisation. Although research on predicting indicators such as traffic flow and speed is relatively sufficient, research on forecasting congestion ratios in different urban regions remains inadequate. Based on traffic big data, this paper proposes a fusion regional congestion ratio prediction model integrating eXtreme gradient boosting tree (XGBoost) and long short‐term memory (LSTM), which integrates multi‐source features, including temporal, meteorological, and spatial factors. First, the XGBoost algorithm is used to model the historical congestion ratios and related features of each region, obtaining preliminary prediction results and extracting regional residual sequences; subsequently, the residual sequences are input into the LSTM network for error correction. Finally, the prediction results of the two stages are fused to obtain more refined regional congestion ratio predictions. Experimental results show that during peak hours on weekdays, taking Region 49 as an example, the MAE of the fusion model is 0.062, the mean absolute percentage error is below 30%, and the comprehensive prediction accuracy reaches up to 72%; under complex weather conditions, for the same region, the RMSE values of the fusion model are 0.048, 0.058, and 0.043, respectively, which are 37%–63% lower than those of the XGBoost model used alone. Feature ablation experiments further verify the key role of temporal, meteorological, and spatial features in improving prediction performance, among which spatial features contribute the most to performance optimisation. This study improves the research framework in the field of urban traffic prediction and provides a theoretical basis and methodological support for regional traffic management practices.