分析疫情不同时期对交通拥堵的影响，用可解释模型揭示行为变化机制。
Interpretable Machine Learning for Traffic Congestion Prediction: Unveiling the Impact of Different COVID-19 Periods
- 构建多时期预测模型，融合疫情、天气等变量，提升复杂场景下的预测能力。
- 双向LSTM在各阶段表现最优，且疫情高峰期间新增病例减少拥堵。
- 首次量化疫情后医疗负担与油价对出行模式的长期影响，适合政策制定者参考。
交通拥堵预测对缓解拥堵至关重要，但新冠疫情及防控措施改变了出行行为，增加了预测难度。本研究针对加州阿拉米达县在解封前、封城期和解封后三个阶段的拥堵情况开展预测。引入天气、季节性及新冠疫情相关变量，采用带交叉验证的递归特征消除法筛选关键特征并防止过拟合。训练并优化支持向量回归（SVR）、多元线性回归、循环神经网络（RNN）和长短期记忆网络（LSTM），其中由于LSTM对超参数敏感，采用自适应选择策略，而SVR和RNN则手动调参。使用归一化均方根误差评估性能。双向LSTM在所有阶段均表现最佳，因其能捕捉双向时间依赖性。通过集成梯度（Integrated Gradients）解释Bi-LSTM预测，使用SHAP（SHapley Additive exPlanations）分析SVR结果。研究发现：封城及解封后阶段，新增确诊病例数对拥堵有主要负向影响，可能源于风险意识提升、自愿减少出行及遵守限行措施；解封后阶段，住院人数上升抑制出行与拥堵，而燃油价格上涨未能阻止私家车使用，反而加剧拥堵。
原文摘要 · Abstract (English)
Traffic congestion prediction is essential for congestion mitigation, but the COVID-19 pandemic and related control measures altered travel behavior and increased prediction complexity. This study predicts congestion in Alameda County, California, during pre-lockdown, lockdown, and post-lockdown periods. Weather, seasonality, and COVID-19 variables are incorporated, and Recursive Feature Elimination with Cross-Validation is used to select important features and reduce overfitting. Support vector regression, multiple linear regression, recurrent neural networks, and long short-term memory networks are trained and optimized. Because LSTM is more sensitive to hyperparameter settings, an adaptive parameter selection approach is used, while SVR and RNN are manually tuned. Performance is evaluated using Normalized Root Mean Square Error. Bidirectional LSTM consistently performs best across all periods because it captures temporal dependence in both directions. Integrated Gradients is used to interpret Bi-LSTM predictions, and SHapley Additive exPlanations is applied to SVR. New COVID-19 cases have a mainly negative effect on congestion during lockdown and post-lockdown, likely due to greater risk awareness, voluntary travel reduction, and compliance with mobility restrictions. In the post-pandemic period, higher hospitalization reduces travel and congestion, while higher fuel prices do not prevent a shift toward private vehicles and therefore increase congestion.
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