Estimating Demand for Online Delivery using Limited Historical Observations
Driven in part by the COVID-19 pandemic, the pace of online purchases for at-home delivery has accelerated significantly. However, responding to this development has been challenging given the lack of public data. The existing data may be infrequent, and a significant portion of data may be missing...
Gespeichert in:
Hauptverfasser: | , , , |
---|---|
Format: | Artikel |
Sprache: | eng |
Schlagworte: | |
Online-Zugang: | Volltext bestellen |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
Zusammenfassung: | Driven in part by the COVID-19 pandemic, the pace of online purchases for
at-home delivery has accelerated significantly. However, responding to this
development has been challenging given the lack of public data. The existing
data may be infrequent, and a significant portion of data may be missing
because of survey participant non-responses. This data paucity renders
conventional predictive models unreliable. We address this shortcoming by
developing algorithms for data imputation and synthetic demand estimation for
future years without the actual ground truth data. We use 2017 Puget Sound
Regional Council (PSRC) and National Household Travel Survey (NHTS) data and
impute from the NHTS for the Seattle-Tacoma-Bellevue MSA where delivery data is
relatively more frequent. Our imputation has the mean-squared error
$\mathsf{MSE} \approx 0.65$ to NHTS with mean $\approx 1$ and standard
deviation $\approx 3.5$ and provides a similarity matching between the two data
sources' samples. Given the unavailability of NHTS data for 2021, we use the
temporal fidelity of PSRC data sources (2017 and 2021) to project the
resolution onto the NHTS providing a synthetic estimate of NHTS deliveries.
Beyond the improved reliability of the estimates, we report explanatory
variables that were relevant in determining the volume of deliveries. This work
furthers existing methods in demand estimation for goods deliveries by
maximizing available sparse data to generate reasonable estimates that could
facilitate policy decisions. |
---|---|
DOI: | 10.48550/arxiv.2209.01457 |