Large urban freight traffic generators (LTGs) are large specialized buildings or landmarks housing multiple establishments and generate a significant truck trips at both disaggregate and aggregate levels. Identification of LTGs and quantifying their relationship with freight travel characteristics helps policymakers formulate necessary logistical interventions and reduce externalities from freight activity. Hence, this study proposes a methodology for identifying LTGs and exploring their interactions on freight travel, expenditure pattern, shipment pattern, and other establishment characteristics. A decision-tree approach called chi-squared automatic interaction detector (CHAID) algorithm is used to map these interactions. Results suggest that LTGs are distinctly associated with multiple variables such as shipment size, shipper expenditure, commodity classification, and business age characteristics. Business age is the best predictor across all models. These associations vary based on LTG definitions. Implications of this study would augment the efforts on interlinking LTGs with urban freight demand modeling systems and enable sustainable city logistics initiatives and last mile delivery management.

大型城市货运交通发电机 (LTG) 是容纳多个机构的大型专业建筑或地标，并在分类和汇总层面产生大量卡车旅行。识别 LTG 并量化它们与货运旅行特征的关系有助于决策者制定必要的后勤干预措施并减少货运活动的外部性。因此，本研究提出了一种方法来识别 LTG 并探索它们在货运旅行、支出模式、运输模式和其他设施特征方面的相互作用。一种称为卡方自动交互检测器 (CHAID) 算法的决策树方法用于映射这些交互。结果表明，LTG 与多个变量明显相关，例如装运规模、托运人支出、商品分类、经营年龄特征。商业时代是所有模型中最好的预测指标。这些关联因 LTG 定义而异。这项研究的意义将加强将 LTG 与城市货运需求建模系统互连的努力，并实现可持续的城市物流计划和最后一英里的交付管理。