End-to-End Prediction of Parcel Delivery Time with Deep Learning for Smart-City

End-to-End Prediction of Parcel Delivery Time with Deep Learning for Smart-City Applications

The acquisition of massive data on parcel delivery motivates postal operators to foster the development of predictive systems to improve customer service. Predicting delivery times successive to being shipped out of the final depot, referred to as last-mile prediction, deals with complicating factors such as traffic, drivers' behaviors, and weather.This work studies the use of deep learning for solving a real-world case of last-mile parcel delivery time prediction. We present our solution under the IoT paradigm and discuss its feasibility on a cloud-based architecture as a smart city application. We focus on a large-scale parcel dataset provided by Canada Post, covering the Greater Toronto Area (GTA). We utilize an origin-destination (OD) formulation, in which routes are not available, but only the start and end delivery points. We investigate three categories of convolutional-based neural networks and assess their performances on the task. We further demonstrate how our modeling outperforms several baselines, from classical machine learning models to referenced OD solutions. Specifically, we show that a ResNet architecture with 8 residual blocks displays the best trade-off between performance and complexity. We perform a thorough error analysis across the data and visualize the deep features learned to better understand the model behavior, making interesting remarks on data predictability. Our work provides an end-to-end neural pipeline that leverages parcel OD data as well as weather to accurately predict delivery durations. We believe that our system has the potential not only to improve user experience by better modeling their anticipation but also to aid last-mile postal logistics as a whole.

深度学习对智慧城市应用的包裹交付时间的端到端预测

邮递业务中海量数据的获取激发了邮政运营商促进预测系统的发展，以改善客户服务。预测从最终仓库运出后的交货时间，称为“最后一英里预测”，涉及交通，驾驶员行为和天气等复杂因素。.. 这项工作研究了深度学习在解决现实世界中最后一英里包裹交付时间预测中的应用。我们将在IoT范式下展示我们的解决方案，并讨论其在基于云的架构上作为智慧城市应用程序的可行性。我们专注于由加拿大邮政提供的大规模包裹数据集，涵盖大多伦多地区（GTA）。我们使用起点（OD）公式，其中路线不可用，而只有起点和终点才可用。我们研究了三类基于卷积的神经网络，并评估了它们在任务上的性能。我们进一步展示了我们的建模如何胜过从经典机器学习模型到参考OD解决方案的几个基准。特别，我们表明，具有8个剩余块的ResNet架构显示了性能和复杂性之间的最佳权衡。我们对数据进行彻底的错误分析，并可视化所学的深层功能，以更好地理解模型行为，并对数据的可预测性进行有趣的评论。我们的工作提供了一个端到端的神经管道，该管道利用包裹的OD数据以及天气来准确地预测交货时间。我们认为，我们的系统不仅有可能通过更好地建模用户的预期来改善用户体验，而且还可以从整体上帮助最后一英里的邮政物流。我们的工作提供了一个端到端的神经管道，该管道利用包裹的OD数据以及天气来准确地预测交货时间。我们认为，我们的系统不仅有可能通过更好地建模用户的预期来改善用户体验，而且还可以从整体上帮助最后一英里的邮政物流。我们的工作提供了一个端到端的神经管道，该管道利用包裹的OD数据以及天气来准确地预测交货时间。我们认为，我们的系统不仅有可能通过更好地建模用户的预期来改善用户体验，而且还可以从整体上帮助最后一英里的邮政物流。 （阅读更多）