用分解方法融合三种模型，提升交通流量预测精度。
Traffic flow forecasting, STL decomposition, Hybrid model, LSTM, ARIMA, XGBoost, Intelligent transportation systems
- 先用STL分解时间序列，再分用LSTM、ARIMA、XGBoost分别处理趋势、周期和残差。
- 在纽约市998条数据上，混合模型在MAE、RMSE、R²上均优于单一模型。
- 适合需要高精度、可解释性的智能交通系统应用。
精准的交通流量预测对智能交通系统和城市交通管理至关重要。然而，单一模型难以捕捉交通数据中复杂的非线性与多尺度时序模式。本研究提出一种基于分解的混合框架，结合季节趋势分解（STL）与三种互补预测模型：利用LSTM建模长期趋势，ARIMA捕捉季节周期性，XGBoost预测非线性残差波动。最终通过乘积方式整合子模型预测结果。基于2015年11月至12月纽约市一个交叉口的998条交通流记录验证，LSTM-ARIMA-XGBoost混合模型在MAE、RMSE和R²指标上显著优于独立的LSTM、ARIMA和XGBoost模型。分解策略有效分离了时序特征，使各模型专精，从而提升预测准确性、可解释性与鲁棒性。
原文摘要 · Abstract (English)
Accurate traffic flow forecasting is essential for intelligent transportation systems and urban traffic management. However, single model approaches often fail to capture the complex, nonlinear, and multi scale temporal patterns in traffic flow data. This study proposes a decomposition driven hybrid framework that integrates Seasonal Trend decomposition using Loess (STL) with three complementary predictive models. STL first decomposes the original time series into trend, seasonal, and residual components. Then, a Long Short Term Memory (LSTM) network models long term trends, an Autoregressive Integrated Moving Average (ARIMA) model captures seasonal periodicity, and an Extreme Gradient Boosting (XGBoost) algorithm predicts nonlinear residual fluctuations. The final forecast is obtained through multiplicative integration of the sub model predictions. Using 998 traffic flow records from a New York City intersection between November and December 2015, results show that the LSTM ARIMA XGBoost hybrid model significantly outperforms standalone models including LSTM, ARIMA, and XGBoost across MAE, RMSE, and R squared metrics. The decomposition strategy effectively isolates temporal characteristics, allowing each model to specialize, thereby improving prediction accuracy, interpretability, and robustness.
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