Multivariate Prediction of Package Delivery Time

包裹递送对于电子商务和许多向消费者递送商品和商品的网上商店来说是一个关键的概念。对于供应商和客户来说，预期的交货时间都是一个重要的因素。包裹递送的时间往往取决于各种参数，从要递送的包裹数量、工作人员和资源的可用性、目的地到仓库的距离，到天气和道路状况。因此，交货时间的准确预测往往是困难的，并受到基于这些高度可变参数的变化的影响。该项目旨在解决这一问题，并基于现有的来自包裹运输、交通和天气数据的多模式数据开发机器学习模型，以高可信度地预测包裹交付时间。Innovapost是加拿大邮政集团公司的一部分，这是一家价值数十亿美元的企业，为加拿大邮政开发和监督整个技术和分析IT基础设施。该项目将允许公司利用开发的方法，以高可信度通知收件人**天/交付时间的窗口，并能够实时更新参数变化的估计。这种精准的服务将会给客户带来更高的满意度，从而为公司带来**的财务增长，最终为加拿大邮政带来**的财务增长。

Delivery of packages is a critical concept for e-commerce and many online stores that deliver merchandise and**goods to consumers. The expected time of delivery is an important factor for vendors and customers alike. The**time of package delivery often depends on a variety of parameters, ranging from the number of packages to be**delivered, the availability of staff and resources, and the distance of the destination to the warehouse, to weather**and road conditions. As a result, exact prediction of time of delivery is often difficult and subject to change based**on these highly variable parameters. This project aims to address this problem and develop machine learning**models based on existing multi-modal data from package shipments, traffic, and weather data, to predict package**delivery times with high confidence. Innovapost is part of The Canada Post Group of Companies, a multi billion**dollar Enterprise, which develops and oversees the entire technical and analytical IT infrastructure for Canada**Post. This project will allow the company to utilize the developed methods to inform recipients of a window for**day/time of delivery with high confidence and be able to update the estimation with changes in the parameters in**real-time. Such an accurate service will result in higher satisfaction rates by customers, which will result in**financial growth by the company and eventually Canada Post.