Spatial dynamics of home delivery and pick-up in online shopping

Abstract

Despite the growing popularity of online shopping, the last-mile delivery method is still a critical problem in the transportation industry. Understanding the choice of order collection methods is important to predicting travel demand, congestion, and emissions. This study investigates the choice of last mile order collection method, which includes 1) home delivery and 2) click and pick up (C&P). Data comes from a superstore chain in the Porto Metropolitan Area from Portugal, which includes 6 months of online grocery order data between January and June 2022 – involving 116,984 orders. The study employs a latent class binary logit model (LBL). The model captures unobserved heterogeneity by assigning individuals into discrete latent classes. Based on goodness-of-fit measures, the model is estimated for two classes. Class 1 predominantly represents consumers in suburban areas, whereas class 2 represents consumers from urban areas. Results reveal that the total number of boxes per order, average commute time, marital status, dwelling status, the proportion of single-parent families, and average distances of bus stop, grocery, and mall contribute to the preference for home delivery and C&P. Results indicate significant heterogeneity between suburban and urban neighborhoods, with suburban renters and suburban married populations showing a lesser preference for home delivery than their urban counterparts. The elasticity effect suggests that the delivery method preference is moderately sensitive to sociodemographic factors, whereas little to zero sensitive to accessibility features. The findings are expected to assist in understanding choices for the last-mile online order collection methods, including areas to prioritize for home delivery and pick-up facilities, as well as developing equitable transportation plans and policies.