对比3类模型在不同预测时长下的表现，发现深度学习更稳、混合模型更有前景。
How does the Performance of the Data-driven Traffic Flow Forecasting Models deteriorate with Increasing Forecasting Horizon? An Extensive Approach Considering Statistical, Machine Learning and Deep Learning Models
- 分统计、机器学习、深度学习三类模型，测试20个时长窗口的交通流预测能力。
- 早期预测中ANFIS-GP最准（RMSE 0.038），中期Bi-LSTM更稳（RMSE 0.1863）。
- 用斜率量化性能下降速度，揭示模型鲁棒性差异，适合交通系统设计者参考。
近年来城市化加速导致交通拥堵加剧。随着交通规划从需求导向转向供给优化，智能交通系统（ITS）成为提升现有基础设施效率的关键。交通预测作为核心功能，支持匝道控制、信号调度与动态导航等应用。本研究基于加州哈博高速公路的实测数据（来自Caltrans PeMS系统），评估统计、机器学习与深度学习模型在交通速度与流量预测上的表现。所有模型在20个预测窗口（最长1小时40分钟）下使用均方根误差（RMSE）、平均绝对误差（MAE）和决定系数（R-Square）进行评估。结果显示，早期预测中ANFIS-GP表现最优（RMSE 0.038，MAE 0.0276，R² 0.9983），而考虑长时依赖的Bi-LSTM在中期预测更具鲁棒性（20分钟预测时RMSE 0.1863，MAE 0.0833，R² 0.987）。通过对性能衰减进行对数变换并计算斜率，量化模型退化程度：深度学习模型中，Bi-LSTM斜率最小（流量预测的RMSE斜率0.0454，MAE 0.0545），显示更强稳定性；而ANFIS-GP斜率较高（流量的RMSE斜率0.1058，MAE 0.1037）。研究建议未来应探索混合模型以提升长期预测能力。
原文摘要 · Abstract (English)
With rapid urbanization in recent decades, traffic congestion has intensified due to increased movement of people and goods. As planning shifts from demand-based to supply-oriented strategies, Intelligent Transportation Systems (ITS) have become essential for managing traffic within existing infrastructure. A core ITS function is traffic forecasting, enabling proactive measures like ramp metering, signal control, and dynamic routing through platforms such as Google Maps. This study assesses the performance of statistical, machine learning (ML), and deep learning (DL) models in forecasting traffic speed and flow using real-world data from California's Harbor Freeway, sourced from the Caltrans Performance Measurement System (PeMS). Each model was evaluated over 20 forecasting windows (up to 1 hour 40 minutes) using RMSE, MAE, and R-Square metrics. Results show ANFIS-GP performs best at early windows with RMSE of 0.038, MAE of 0.0276, and R-Square of 0.9983, while Bi-LSTM is more robust for medium-term prediction due to its capacity to model long-range temporal dependencies, achieving RMSE of 0.1863, MAE of 0.0833, and R-Square of 0.987 at a forecasting of 20. The degradation in model performance was quantified using logarithmic transformation, with slope values used to measure robustness. Among DL models, Bi-LSTM had the flattest slope (0.0454 RMSE, 0.0545 MAE for flow), whereas ANFIS-GP had 0.1058 for RMSE and 0.1037 for flow MAE. The study concludes by identifying hybrid models as a promising future direction.
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