Local Spatial Analysis of the Crash Frequency of Food Delivery Motorcyclists vs. Nondelivery Motorcyclists in relation to Points of Interest
Bibliographic record
Abstract
The COVID-19 pandemic has increased the demand for online food delivery services (OFDS), leading to an increase in related crashes over the last few years. While recent studies have focused on nonmotorised vehicles (such as bicycles or e-bikes), few researchers have examined the role of motorcycles and the possible spatial relationships with various points of interest (POIs). In addition, most crash and POIs studies have utilized typical restaurant datasets instead of specific restaurants partnered with OFDS, which might bias the impact of traffic safety estimation. To address these gaps, a geographically weighted negative binomial model (GWNBR) was used to determine the factors contributing to OFDS-related motorcycle accidents and account for spatial heterogeneity. The results indicated that areas with more restaurants, intersections, and shopping malls (only significant on weekends) tended to have more OFDS motorcycle crashes. The results should inspire more effective policies for delivery drivers, given the increasing popularity of OFDS.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".