Spatial dynamics of home delivery and pick-up in online shopping
Bibliographic record
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".