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

Chandra, A and Pani, A and Sahu, PK and Majumdar, BB and Sharma, S (2021) Identifying large freight traffic generators and investigating the impacts on travel pattern: A decision tree approach for last-mile delivery management. In: Research in Transportation Business and Management .

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Official URL: https://doi.org/10.1016/j.rtbm.2021.100695

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. © 2021 Elsevier Ltd

Item Type: Journal Article
Publication: Research in Transportation Business and Management
Publisher: Elsevier Ltd
Additional Information: The copyright for this article belongs to Elsevier Ltd
Department/Centre: Division of Mechanical Sciences > Civil Engineering
Date Deposited: 16 Nov 2021 10:41
Last Modified: 16 Nov 2021 10:41
URI: http://eprints.iisc.ac.in/id/eprint/69692

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