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Long-term multi-step traffic flow prediction is crucial for effective traffic management. Recently, XGBoost has demonstrated its capability in multi-step prediction applications. However, optimal hype...rparameter tuning using adaptive intelligent methods like the Particle Swarm Optimization (PSO) algorithm has yet to be explored. This paper presents an Adaptive eXtreme Gradient Boosting (Adaptive-XGBoost) model utilising the direct method with the combination of Grid Search and PSO algorithms for adaptive hyperparameter tuning for multi-step prediction. This approach aims to demonstrate the potential of Adaptive-XGBoost compared to existing deep learning models like Long- Short Term Memory (LSTM) and Transformer in terms of Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). Experimental results show that the Adaptive XGBoost model achieved a 3.10% and 4.98% improvement in MAE over the Transformer and LSTM models, respectively, and a 0.26% and 5.69% improvement in RMSE. These findings highlight the potential of Adaptive-XGBoost for improved long-term multi-step traffic flow prediction.続きを見る
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