论文标题
建模房屋交付的频率：在Covid-19 pandemics期间多伦多的诱发旅行需求贡献
Modelling the Frequency of Home Deliveries: An Induced Travel Demand Contribution of Aggrandized E-shopping in Toronto during COVID-19 Pandemics
论文作者
论文摘要
19009年大流行极大地催化了电子购物者的扩散。电子购物的急剧增长无疑会对旅行需求产生重大影响。结果，运输建模者对电子购物需求建模的能力变得越来越重要。这项研究开发了预测家庭每周的送货频率的模型。我们使用经典计量经济学和机器学习技术来获得最佳模型。据发现，诸如拥有在线杂货会员资格，家庭成员的平均年龄，男性家庭成员的百分比，家庭工人的百分比以及各种土地使用因素等社会经济因素会影响送货上门的需求。这项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表现。在通过机器学习和计量经济学模型确定的变量效果中找到了一致性。但是，具有相似的召回精度，有序的概率模型是一种经典的计量经济学模型，可以准确预测家庭交付需求的总分布。相反，两个机器学习模型都无法匹配观察到的分布。
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.
