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.

中文翻译：

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在 COVID-19 疫情期间使用有限的数据生成在线货运需求


城市货运数据分析对于做出明智的决策、资源分配和优化路线至关重要，从而在城市中实现高效和可持续的货运运营。部分受 COVID-19 大流行的推动，在线购买送货上门的步伐显着加快。然而，由于缺乏公共数据，应对这一发展一直具有挑战性。由于调查参与者未回答，现有数据可能不常见。这种数据的缺乏使传统的预测模型不可靠。