Comparison of Parametric and Nonparametric Hazard Models for Stop Durations on Urban Tours with Commercial Vehicles
Bibliographic record
Abstract
Hazard-based stop duration models for the stop duration of commercial vehicles in urban areas are presented. Passively collected GPS data were used to estimate two hazard-survival models to predict the stop duration of commercial vehicles undertaking urban pickup and delivery tours. The first was an accelerated failure-time parametric hazard model, and the second was a proportional nonparametric hazard model. Explanatory variables included time of day, population or employment density, number of stops, distance of inbound and outbound trips, and attributes of destination establishments, such as sales volume and industry classification. Models were estimated with and without establishment data because these data may not always be available for model application. Results showed that passively collected GPS data could be used to estimate stop duration models when linked with other data sources that provided appropriate explanatory variables. The parametric models were shown to outperform the nonparametric models and had higher measures of goodness of fit and better hazard distributions for stop duration.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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".