Identifying large freight traffic generators and investigating the impacts on travel pattern: A decision tree approach for last-mile delivery management

Abstract

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.