Traffic flow forecasting, STL decomposition, Hybrid model, LSTM, ARIMA, XGBoost, Intelligent transportation systems

In the evolution of Intelligent Transportation Systems (ITS), traffic flow prediction has played a pivotal role [1]. Accurate and real-time traffic forecasting is not only a fundamental component of ITS but also a key enabler for efficient urban operation and intelligent mobility development [2, 3]. With the rapid increase in private vehicle ownership, particularly in fast-growing economies, urban road networks have become increasingly congested, and major intersections and arterial roads often experience persistent traffic jams [4]. By accurately predicting traffic flow over short time intervals at critical intersections, transportation authorities can make informed decisions on traffic control and road planning, reduce accidents and delays, and provide travelers with reasonable route recommendations, thereby alleviating traffic pressure and maximizing the utilization of road resources. Figure 1 shows the traffic flow distribution scene at a typical four-way intersection on a city road. In traditional traffic flow prediction studies, various modeling approaches have been proposed, ranging from classical time series models (such as ARIMA) to machine learning and deep learning frameworks (such as RNN and LSTM) [5]. Although these single-model approaches can achieve satisfactory planning performance under controlled conditions [6], their generalization and robustness are often limited by the highly dynamic and nonlinear nature of urban traffic systems [7]. Moreover, most existing models primarily emphasize prediction accuracy while overlooking critical aspects such as computational efficiency, adaptability, and scalability, which are essential for real-time applications in large-scale traffic networks [8]. To address the aforementioned limitations, hybrid and decomposition-based modeling approaches have attracted growing research interest.