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

Author

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

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.

Suggested Citation

  • Mirzanezhad, Majid & Twumasi-Boakye, Richard & Fabusuyi, Tayo & Broaddus, Andrea, 2024. "Generating online freight delivery demand during COVID-19 using limited data," Transportation Research Part B: Methodological, Elsevier, vol. 190(C).
    • Handle: RePEc:eee:transb:v:190:y:2024:i:c:s0191261524002248
    DOI: 10.1016/j.trb.2024.103100
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    References listed on IDEAS

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