标题： 建模家庭分娩的频率：在新冠疫情期间多伦多扩增电子商务对诱导出行需求的贡献 显示英文标题

标题： Modelling the Frequency of Home Deliveries: An Induced Travel Demand Contribution of Aggrandized E-shopping in Toronto during COVID-19 Pandemics

摘要： 新冠疫情极大地加速了电子商务购物的普及。 电子商务购物的显著增长无疑会对出行需求产生重大影响。 因此，交通建模者对电子商务购物需求进行建模的能力变得越来越重要。 本研究开发了模型来预测家庭每周的送货频率。 我们使用了经典的计量经济学和机器学习技术来获得最佳模型。 研究发现，社会经济因素，如是否拥有在线杂货会员、家庭成员的平均年龄、男性家庭成员的比例、家庭中的工作人员数量以及各种土地利用因素，都会影响送货需求。 本研究还比较了机器学习模型和经典计量经济学模型的解释和性能。 通过机器学习和计量经济学模型识别的变量影响达成了共识。 然而，在相似的召回准确率下，有序概率模型（一种经典的计量经济学模型）能够准确预测家庭送货需求的总体分布。 相反，两种机器学习模型都无法匹配观察到的分布。 显示英文摘要

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.