基于成对注意力指针神经网络的最后一公里配送驾驶员路径轨迹预测
Predicting Drivers' Route Trajectories in Last-Mile Delivery Using A Pair-wise Attention-based Pointer Neural Network
AI总结：
本文提出一种基于成对注意力的指针神经网络，利用驾驶员历史轨迹预测最后一公里配送的实际停靠点顺序，并通过迭代序列生成算法优化首站选择，在亚马逊真实数据上显著优于传统优化方法和LSTM等基线模型。
摘要：
在最后一公里配送中，驾驶员经常偏离计划配送路线，因为他们对道路和路边基础设施、客户可用性以及各自服务区域的其他特征具有隐性知识。因此，在实际运营条件下，经验丰富的人类驾驶员所选择的实际停靠点顺序可能比理论上的最短距离路线更可取。因此，能够预测人类驾驶员会遵循的实际停靠点顺序，有助于改进最后一公里配送中的路线规划。本文针对这一预测任务，利用驾驶员的历史配送轨迹数据，提出了一种基于成对注意力的指针神经网络。除了常用的用于序列到序列预测的编码器-解码器架构外，我们提出了一种新的注意力机制，该机制基于一个替代的特定神经网络来捕获每对停靠点之间的局部成对信息。为了进一步捕获路线的全局效率，我们提出了一种新的迭代序列生成算法，该算法在模型训练后使用，以识别产生最低运营成本的路线第一个停靠点。基于亚马逊在美国最后一公里配送运营的真实运营数据的大量案例研究结果表明，我们提出的方法在寻找更接近经验丰富的驾驶员在现场执行的高质量路线的停靠点顺序方面，能够显著优于传统的基于优化的方法和其他机器学习方法（如长短期记忆编码器-解码器和原始指针网络）。与基准模型相比，所提出的模型可以将前四个停靠点的平均预测准确率从约0.229提高到0.312，并将预测路线与实际路线之间的差异减少约15%。
英文摘要：
In last-mile delivery, drivers frequently deviate from planned delivery routes because of their tacit knowledge of the road and curbside infrastructure, customer availability, and other characteristics of the respective service areas. Hence, the actual stop sequences chosen by an experienced human driver may be potentially preferable to the theoretical shortest-distance routing under real-life operational conditions. Thus, being able to predict the actual stop sequence that a human driver would follow can help to improve route planning in last-mile delivery. This paper proposes a pair-wise attention-based pointer neural network for this prediction task using drivers' historical delivery trajectory data. In addition to the commonly used encoder-decoder architecture for sequence-to-sequence prediction, we propose a new attention mechanism based on an alternative specific neural network to capture the local pair-wise information for each pair of stops. To further capture the global efficiency of the route, we propose a new iterative sequence generation algorithm that is used after model training to identify the first stop of a route that yields the lowest operational cost. Results from an extensive case study on real operational data from Amazon's last-mile delivery operations in the US show that our proposed method can significantly outperform traditional optimization-based approaches and other machine learning methods (such as the Long Short-Term Memory encoder-decoder and the original pointer network) in finding stop sequences that are closer to high-quality routes executed by experienced drivers in the field. Compared to benchmark models, the proposed model can increase the average prediction accuracy of the first four stops from around 0.229 to 0.312, and reduce the disparity between the predicted route and the actual route by around 15%.
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。
