标题： DeepSTA：一种在异常条件下用于物流配送准时率预测的空间-时间注意力网络 显示英文标题

标题： DeepSTA: A Spatial-Temporal Attention Network for Logistics Delivery Timely Rate Prediction in Anomaly Conditions

摘要： 提前预测快递员的准时配送率对于物流行业至关重要，使公司能够采取预防措施以确保配送服务的正常运行。 这在异常情况（如疫情爆发）期间尤为重要，在这种情况下，快递员的准时配送率会明显下降并且波动显著。 现有研究较少关注物流场景。 此外，许多专注于异常场景下预测任务的研究未能明确建模异常事件，例如，将外部因素与其他特征同等对待，导致信息大量丢失。 进一步地，由于某些异常事件发生频率较低，传统基于数据驱动的方法在这些场景中表现不佳。 为了解决这些问题，我们提出了一种深度时空注意力模型，称为DeepSTA。 具体而言，为了避免信息损失，我们设计了一个异常时空学习模块，该模块使用循环神经网络来建模事件信息。 此外，我们利用Node2vec来建模道路区域之间的相关性，并采用图神经网络和长短时记忆来捕捉快递员的时空依赖关系。 为了应对异常情况下训练数据不足的问题，我们提出了一个异常模式注意力模块，该模块通过注意力机制采用记忆网络来存储快递员的异常特征模式。 2022年新冠疫情期间真实物流数据集上的实验表明，该模型比12.11个多竞争基线的表现更好。 显示英文摘要

摘要： Prediction of couriers' delivery timely rates in advance is essential to the logistics industry, enabling companies to take preemptive measures to ensure the normal operation of delivery services. This becomes even more critical during anomaly conditions like the epidemic outbreak, during which couriers' delivery timely rate will decline markedly and fluctuates significantly. Existing studies pay less attention to the logistics scenario. Moreover, many works focusing on prediction tasks in anomaly scenarios fail to explicitly model abnormal events, e.g., treating external factors equally with other features, resulting in great information loss. Further, since some anomalous events occur infrequently, traditional data-driven methods perform poorly in these scenarios. To deal with them, we propose a deep spatial-temporal attention model, named DeepSTA. To be specific, to avoid information loss, we design an anomaly spatio-temporal learning module that employs a recurrent neural network to model incident information. Additionally, we utilize Node2vec to model correlations between road districts, and adopt graph neural networks and long short-term memory to capture the spatial-temporal dependencies of couriers. To tackle the issue of insufficient training data in abnormal circumstances, we propose an anomaly pattern attention module that adopts a memory network for couriers' anomaly feature patterns storage via attention mechanisms. The experiments on real-world logistics datasets during the COVID-19 outbreak in 2022 show the model outperforms the best baselines by 12.11% in MAE and 13.71% in MSE, demonstrating its superior performance over multiple competitive baselines.