  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.
