2020/09/23 by Arthur Cruz de Araujo, Ali Etemad, de Araujo, Arthur Cruz +1 · 2 citations
Engineering · Social Sciences · #FOS: Computer and information sciences #FOS: Electrical engineering #Machine Learning (cs.LG) #Signal Processing (eess.SP) #Traffic Prediction and Management Techniques #Transportation Planning and Optimization #Urban and Freight Transport Logistics #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2009.12197
openalex publication_date 2020/09/23 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
The acquisition of massive data on parcel delivery motivates postal operators\nto foster the development of predictive systems to improve customer service.\nPredicting delivery times successive to being shipped out of the final depot,\nreferred to as last-mile prediction, deals with complicating factors such as\ntraffic, drivers' behaviors, and weather. This work studies the use of deep\nlearning for solving a real-world case of last-mile parcel delivery time\nprediction. We present our solution under the IoT paradigm and discuss its\nfeasibility on a cloud-based architecture as a smart city application. We focus\non a large-scale parcel dataset provided by Canada Post, covering the Greater\nToronto Area (GTA). We utilize an origin-destination (OD) formulation, in which\nroutes are not available, but only the start and end delivery points. We\ninvestigate three categories of convolutional-based neural networks and assess\ntheir performances on the task. We further demonstrate how our modeling\noutperforms several baselines, from classical machine learning models to\nreferenced OD solutions. Specifically, we show that a ResNet architecture with\n8 residual blocks displays the best trade-off between performance and\ncomplexity. We perform a thorough error analysis across the data and visualize\nthe deep features learned to better understand the model behavior, making\ninteresting remarks on data predictability. Our work provides an end-to-end\nneural pipeline that leverages parcel OD data as well as weather to accurately\npredict delivery durations. We believe that our system has the potential not\nonly to improve user experience by better modeling their anticipation but also\nto aid last-mile postal logistics as a whole.\n