Generating online freight delivery demand during COVID-19 using limited data

Majid Mirzanezhad, Richard Twumasi-Boakye, Tayo Fabusuyi and Andrea Broaddus

Transportation Research Part B: Methodological, 2024, vol. 190, issue C

Abstract: Urban freight data analysis is crucial for informed decision-making, resource allocation, and optimizing routes, leading to efficient and sustainable freight operations in cities. 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 because of survey participant non-responses. This data paucity renders conventional predictive models unreliable.

Keywords: Online freight delivery; Data replication; Travel survey data; Limited data (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1016/j.trb.2024.103100

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