Dynamic Choice Model of Urban Commercial Activity Patterns of Vehicles and People
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Intraurban commercial vehicle travel is a relatively underdeveloped aspect of urban travel demand modeling despite the large share of the weekday traffic stream represented by commercial movements. One problem is the proprietary nature of these data and the corresponding lack of behavioral understanding of how establishments schedule their trips. Even when such data have been made available, such as through establishment travel surveys, the large variation in firm size, commodities and services, and logistics practices makes it difficult to create a generalized decision framework. This work uses establishment survey data collected by the Ohio Department of Transportation to create an intraurban commercial vehicle model to be run in a disaggregate microsimulation environment and focuses on commercial movement patterns. The model generates entire daily patterns for workers who regularly travel as part of their jobs and creates tours through a dynamic choice process that incrementally builds tours, taking into consideration elapsed time and time of day in next-stop purpose and location choices. Activity durations are embedded in the utility equations of “stay” alternatives and provide internal consistency between the dimensions of activity purpose, duration, time of day, and location. Model formulation and estimation results are presented for the dynamic activity choice model component. The model system can reproduce observed commercial travel patterns found in the survey data and provide intuitively plausible interpretations for commercial travel behavior in the absence of more detailed knowledge of individual and firm operations.
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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 | 0.007 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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 it