MMCAformer：用于基于微观连接车辆驾驶行为的交通速度预测的宏微交叉注意力变换器
MMCAformer: Macro-Micro Cross-Attention Transformer for Traffic Speed Prediction with Microscopic Connected Vehicle Driving Behavior
AI总结：
MMCAformer通过结合宏观交通特征和微观驾驶行为特征，提升交通速度预测的准确性和不确定性估计。
摘要：
准确的速度预测对于主动交通管理以提高交通效率和安全性至关重要。现有研究主要依赖于聚合的宏观交通流量数据来预测未来的交通趋势，而道路交通动态也受到个体微观人类驾驶行为的影响。最近的连接车辆（CV）数据提供了丰富的驾驶行为特征，为将这些行为洞察纳入速度预测提供了新的机会。为此，我们提出了宏微交叉注意力变换器（MMCAformer），以将基于CV数据的微观驾驶行为特征与宏观交通特征结合起来进行速度预测。具体而言，MMCAformer使用自注意力学习宏观交通流量中的内在依赖关系，并使用交叉注意力捕捉宏观交通状态与微观驾驶行为之间的时空交互。MMCAformer通过学生t分布负对数似然损失进行优化，以提供点状速度预测并估计不确定性。在四个佛罗里达州高速公路上的实验表明，所提出的MMCAformer相比基线方法具有优越的性能。与仅使用宏观特征相比，引入微观驾驶行为特征不仅提高了预测准确性（例如，总体RMSE、MAE和MAPE分别降低了9.0%、6.9%和10.2%），还缩小了模型预测的不确定性（例如，四个高速公路的平均预测区间减少了10.1-24.0%）。结果表明，紧急刹车和加速频率成为最显著的特征。这些改进在拥堵、低速交通条件下更为明显。
英文摘要：
Accurate speed prediction is crucial for proactive traffic management to enhance traffic efficiency and safety. Existing studies have primarily relied on aggregated, macroscopic traffic flow data to predict future traffic trends, whereas road traffic dynamics are also influenced by individual, microscopic human driving behaviors. Recent Connected Vehicle (CV) data provide rich driving behavior features, offering new opportunities to incorporate these behavioral insights into speed prediction. To this end, we propose the Macro-Micro Cross-Attention Transformer (MMCAformer) to integrate CV data-based micro driving behavior features with macro traffic features for speed prediction. Specifically, MMCAformer employs self-attention to learn intrinsic dependencies in macro traffic flow and cross-attention to capture spatiotemporal interplays between macro traffic status and micro driving behavior. MMCAformer is optimized with a Student-t negative log-likelihood loss to provide point-wise speed prediction and estimate uncertainty. Experiments on four Florida freeways demonstrate the superior performance of the proposed MMCAformer compared to baselines. Compared with only using macro features, introducing micro driving behavior features not only enhances prediction accuracy (e.g., overall RMSE, MAE, and MAPE reduced by 9.0%, 6.9%, and 10.2%, respectively) but also shrinks model prediction uncertainty (e.g., mean predictive intervals decreased by 10.1-24.0% across the four freeways). Results reveal that hard braking and acceleration frequencies emerge as the most influential features. Such improvements are more pronounced under congested, low-speed traffic conditions.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
