Modelling the Frequency of Home Deliveries: An Induced Travel Demand Contribution of Aggrandized E-shopping in Toronto during COVID-19 Pandemics
疫情期间多伦多家庭配送频率建模与网购诱发出行需求AI 解读 ↓
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AI 根据所提供的论文材料归类：摘要明确使用有序 probit 建模家庭配送频率，直接对应有序离散结果模型。
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疫情期间多伦多家庭配送频率建模与网购诱发出行需求
- 研究问题
- 疫情推动网购增长后，哪些家庭与土地利用特征关联每周配送频率，传统计量模型和机器学习能否准确预测其总体分布？
- 方法与思路
- 作者使用经典计量与机器学习方法预测家庭每周配送频率，比较变量解释、召回准确性及总体分布拟合，并考察有序 probit。
- 材料陈述的贡献
- 摘要称，各模型对变量作用的判断相符；召回准确性相近时，有序 probit 能较准确预测总体配送分布，两种机器学习模型未能匹配。
- 阅读关注点
- 应核对配送频率的分组、样本与预测验证设计、机器学习模型及调参方式，并区分个体分类表现和总体分布拟合表现。
- 阅读边界
- 这是 arXiv 预印本，同行评审或期刊接收未经核实。摘要未说明具体模型、数据规模及验证细节，变量关联不能直接解释为因果作用。
原文摘要
The COVID-19 pandemic dramatically catalyzed the proliferation of e-shopping. The dramatic growth of e-shopping will undoubtedly cause significant impacts on travel demand. As a result, transportation modeller's ability to model e-shopping demand is becoming increasingly important. This study developed models to predict household' weekly home delivery frequencies. We used both classical econometric and machine learning techniques to obtain the best model. It is found that socioeconomic factors such as having an online grocery membership, household members' average age, the percentage of male household members, the number of workers in the household and various land use factors influence home delivery demand. This study also compared the interpretations and performances of the machine learning models and the classical econometric model. Agreement is found in the variable's effects identified through the machine learning and econometric models. However, with similar recall accuracy, the ordered probit model, a classical econometric model, can accurately predict the aggregate distribution of household delivery demand. In contrast, both machine learning models failed to match the observed distribution.