Predicting the Last Mile: Route-Free Prediction of Parcel Delivery Time with Deep Learning for Smart-City Applications
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Authors
Cruz de Araujo, Arthur
Date
Type
thesis
Language
eng
Keyword
last-mile , parcel delivery , origin-destination , predictive modeling , deep learning
Alternative Title
Abstract
The massive acquisition of parcel data motivates postal operators to foster the development of predictive systems for better customer service. Predicting delivery after the parcels are sent out of the final depot, referred to as \textit{last-mile} prediction, deals with complicating factors such as traffic, drivers' behaviors, and weather conditions. Our work provides an end-to-end neural pipeline that leverages parcel and weather data to accurately predict delivery durations. We present our solution under the IoT paradigm and discuss its feasibility on a cloud-based architecture as a smart city application. We utilize a route-free origin-destination (OD) formulation, only relying on the delivery start and end points. We use a large-scale real-world dataset provided by Canada Post, containing last-mile information of parcels delivered in the Greater Toronto Area in the first half of 2017. We investigate different types of convolutional neural networks and demonstrate how our models outperform several baselines, from classical machine learning models to referenced solutions. Specifically, we show that a ResNet network with 8 residual blocks displays the best performance-complexity trade-off. We provide a thorough error analysis and visualize the features learned to better understand the model behavior, with remarks on data predictability. Our system has the potential to improve the user experience by better modeling their anticipation and to aid last-mile postal logistics as a whole.
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Queen's University's Thesis/Dissertation Non-Exclusive License for Deposit to QSpace and Library and Archives Canada
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Intellectual Property Guidelines at Queen's University
Copying and Preserving Your Thesis
This publication is made available by the authority of the copyright owner solely for the purpose of private study and research and may not be copied or reproduced except as permitted by the copyright laws without written authority from the copyright owner.
CC0 1.0 Universal
ProQuest PhD and Master's Theses International Dissemination Agreement
Intellectual Property Guidelines at Queen's University
Copying and Preserving Your Thesis
This publication is made available by the authority of the copyright owner solely for the purpose of private study and research and may not be copied or reproduced except as permitted by the copyright laws without written authority from the copyright owner.
CC0 1.0 Universal