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.
翻译:获取关于包裹交付的大量数据,促使邮政运营商推动开发预测系统以改善客户服务。预测交货时间,直到运出最后仓库,称为最后一英里预测,涉及交通、司机行为和天气等复杂因素。这项工作研究如何利用深层学习解决现实世界中最后一英里包裹交付时间预测案例。我们在IOT模式下提出我们的解决方案,并讨论以云为基础的建筑作为智能城市应用程序的可行性。我们侧重于加拿大邮政局提供的大型包裹数据集,涵盖大多伦多地区(GTA)。我们使用原产地目的地(OD)配方,其中没有路线,但只有起始点和终点交货点。我们调查了三种基于革命的神经网络类别,并评估了它们在任务中的表现。我们进一步展示了我们的模型如何超越从经典机器学习模型到引用疾病解决方案等数个基线。我们展示了由8个残余块组成的ResNet架构,展示了业绩和复杂程度之间的最佳贸易模式。我们还使用了原产地目的地(ODD)配方(OD)配方配方配方配方配方配方配方配方配方配方配方配方配方配方配方配方配方配方配方配方配方配方配方配方配方配方配方配方配方配方配方配方,其中,其中的配方配方配方配方配方配方配方配方配方配方配方配方配方配方配方配方配方,其中方配方配方配方配方配方配方配方配方配方配方配方配方配方配方配方配方配方配方配方配方配方配方,其中没有最佳,其中,其中没有最佳贸易和方配方配方配方配方配方配方配方配方配方配方配方配方配方配方配方配方配方配方配方称方,没有方,没有方,没有方,没有行方配方配方,没有行方,没有行方,没有行方,配方,没有路,没有行方,没有行方,没有行方,配方,配方,配方,没有行方,没有行方,没有路,没有行方,没有行方,没有行方,只有线,只有线,只有路,只能,只能,只能,