Traffic flow forecasting, STL decomposition, Hybrid model, LSTM, ARIMA, XGBoost, Intelligent transportation systems
PositiveArtificial Intelligence
A new study highlights the importance of accurate traffic flow forecasting for intelligent transportation systems and urban traffic management. It introduces a hybrid framework that combines Seasonal Trend decomposition using Loess (STL) with advanced predictive models like LSTM, ARIMA, and XGBoost. This approach aims to better capture the complex patterns in traffic data, which is crucial for improving traffic management and reducing congestion.
— Curated by the World Pulse Now AI Editorial System



