Order acceptance choice modeling of crowd-sourced delivery services: a systematic comparative study
Bibliographic record
Abstract
<p>The efficiency of crowd-sourced delivery services (CDS) like UberEats and AmazonFlex highly depends on the decisions of individual shippers. Operating as freelancers, these shippers have the freedom to accept or decline orders from the CDS platform. Their decisions not only affect their earnings and the waiting times for orders but also influence the platforms’ overall revenue and reputation. Understanding the factors that shape these decisions is thus crucial. In our study, we gather data from CDS shippers in Shanghai, China, using stated preference surveys. We then design a discrete choice model to predict shippers’ behaviors and compare its accuracy, computational efficiency, and interpretability with five commonly used machine learning methods. Our analyses reveal that the Extreme Gradient Boosting (XGB) model and Random Forests (RFs) model outperform other models in prediction accuracy, achieving f1 scores of 69.3% and 65% respectively. Notably, our per- mutation importance analysis indicate that the shipper’s age, income, and the compensation awarded per order are the most influential determinants in their decision to accept or decline orders.</p>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".