Spatio‐Temporal Object‐Oriented Data Model for Disaggregate Travel Behavior
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
The research field of transportation demand forecasting has started to focus on disaggregate travel behavior and micro‐simulation models. To create data infrastructure, disaggregate trip surveys are conducted and large numbers of observations are collected. To efficiently exploit these surveys, the transfer of the individual trip data to a GIS must start with the development of a solid conceptual data model that fully captures the semantic richness of the application domain and emphasizes its spatio‐temporal properties. This paper presents a data modeling process that is based on a combination of complex system theory and the object‐oriented paradigm and produced an object‐oriented spatio‐temporal data model. Main domain entities are modeled as highly structured classes. They encapsulate a memory of their time bound connections and states. Observation data sets are sampled from the origin‐destination survey conducted in the Québec region in 1991. This survey incorporated street networks and activity places. The model was smoothly implemented into a proof‐of‐concept database prototype hosted by an object‐oriented GIS shell. The prototype offers a means to navigate through a nested hierarchy of objects, providing a description of an individual’s travel behavior over space and time. The objects have a solid conceptual basis and can meet the needs of scientific research such as hypothesis formulation, simulation, forecasting and induction.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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