发表机构
National University of Singapore（新加坡国立大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究以阿拉米达县为对象，采用多种机器学习模型预测不同疫情时期的交通拥堵，通过可解释方法揭示疫情相关因素对拥堵的影响，发现双向LSTM性能最优。
AI 中文摘要
交通拥堵预测对缓解拥堵至关重要，但新冠疫情及相关管控措施改变了出行行为，增加了预测复杂性。本研究针对美国加利福尼亚州阿拉米达县在疫情前封锁期、封锁期和后封锁期的拥堵情况进行预测，纳入天气、季节性和新冠疫情变量，采用带交叉验证的递归特征消除法选择重要特征并减少过拟合。训练并优化了支持向量回归、多元线性回归、循环神经网络和长短期记忆网络，因LSTM对超参数设置更敏感，采用自适应参数选择方法，而SVR和RNN则手动调参。采用归一化均方根误差评估性能，双向LSTM在所有时期均表现最佳，因其能捕捉双向时间依赖关系。使用Integrated Gradients解释Bi-LSTM预测，对SVR应用SHapley Additive exPlanations。封锁期和后封锁期新增新冠病例对拥堵主要产生负面影响，可能源于风险意识提升、主动减少出行及遵守出行限制；后疫情期，住院人数增加减少出行与拥堵，而燃油价格上涨未能阻止向私家车转移，反而加剧拥堵。
英文摘要
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.
