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

Highlights

• Research identifies large urban freight traffic generators (LTGs) using a decision-tree approach.

• Examines the linkage between LTGs and freight travel characteristics.

• Models predict an establishment to be defined as either LTG or Non-LTG.

• Business age is found to be the best predictor of LTGs.

• Significant implications towards sustainable city logistics initiatives such as off-hour deliveries.

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.

Introduction

Contrary to public perceptions, “large” intermodal facilities such as ports and container terminals only represent 2 to 6% of the delivery truck traffic in urban areas. Individual businesses or agglomeration of businesses in urban areas, on the other hand, individually or collectively generate 90 to 94% proportion of total truck trips. Identification of such “large urban freight traffic generators” (LTGs) or “large commercial traffic generators” offers great opportunities for the development and implementation of city logistics initiatives aimed at mitigating externalities of freight such as congestion, noise, and pollution (Jaller, Wang, Holguín-veras, Journal, & Use, 2015; Pani, Sahu, & Holguín-Veras, 2021). LTGs can be classified into two major groups (Holguín-Veras et al., 2013; Jaller et al., 2015; Jaller, Holguín-Veras, & Hodge, 2013; Lawson et al., 2012) - first, large buildings (e.g., Empire State Building) or landmarks that accommodate multiple establishments and second, large businesses (mixed land use buildings, universities, hospitals, etc.) that generate a significant amount of freight. These facilities provide numerous opportunities for the development and implementation of city logistic initiatives, such as off-hour deliveries or autonomous delivery robot (ADR) initiatives (Pani, Mishra, Golias, & Figliozzi, 2020). These facilities can act as centralized receiving stations to consolidate and distribute shipments without time constraints (Jaller et al., 2015). LTGs are ideal for implementing unattended delivery strategies under adequate staffing and security considerations (Holguín-Veras et al., 2013). They may also provide many opportunities for parking and loading-unloading initiatives (Jaller et al., 2013, Jaller et al., 2015; Silva, da Silva Lima, Alves, Yushimito, & Holguín-Veras, 2020). These logistical initiatives, coupled together, offer positive impacts on quality of life, and reduce the externalities caused by urban freight movements. Therefore, identifying LTGs and quantifying their distinctions in freight travel characteristics would help develop appropriate planning and policy measures to design efficient city logistics initiatives.

In previous research, Jaller et al. (2015) introduced two effective procedures to identify LTGs: (i) large buildings and landmarks, (ii) large establishments. LTGs are identified based on variables that can define the measure of business size and capture the scale and intensity of freight travel. Large buildings and landmarks are identified as organizations housing scores of establishments and equipped with central receiving stations. Commercial area and employment size are used to identify large establishments. The role of these variables in modeling the scale and intensity of freight generation and freight trip generation is also well-captured in previous studies (Giuliano, 2014; Guerrero & Proulhac, 2014; Holguín-Veras, Sánchez-Díaz, et al., 2013; Lawson et al., 2012; Pani, Sahu, Patil, & Sarkar, 2018). However, while an initial study exists to identify LTGs, there is a lack of studies investigating their distinct impacts on freight travel characteristics. To address this research gap and expand the existing freight research literature, this research a) identifies LTGs and b) analyzes their distinct impacts on freight travel, expenditure pattern, shipment establishment characteristics. Specifically, the aim is to answer the following research questions:

• What are all the freight establishment and travel characteristics that can be influenced by LTGs?

• How do we map or quantify interplay of LTGs with freight establishment and travel characteristics?

• What extent these relationships vary with respect to different LTG definitions?