Macro-, Meso-, and Microlevel Validation of an Activity-Based Travel Demand Model
Bibliographic record
Abstract
Validation is an essential part of the model development process for practical applications in the real world context. Spatial transferability has been recognized as a useful validation test for travel demand models. To date, however, transferability of activity-based models has not been frequently assessed. This paper focuses on how and at which level the validation of activity-based travel demand models has been performed. More specifically, it examines the spatial transferability of an activity-based model, TASHA (Travel Activity Scheduler for Household Agents), which has been developed for the Greater Toronto Area (GTA), Canada. It applies TASHA to the Island of Montreal, Canada, using the 2003 Origin-Destination (O-D) travel survey and the 2001 Canadian census, and validates the transfer by comparing modelled and observed activity attributes from the 2003 O-D survey for five different activities (work, school, shopping, other, and return to home) at three different levels of aggregation, macro-level (aggregation of the entire population), meso-level (aggregation by population segments by age group and gender, and by home location), and micro-level (individuals). In most of the cases, simulated results are significantly different than the observed activity travel patterns of the individuals of this region. The large variations found at different levels most likely indicate differences in behaviours between Montreal and Toronto. The authors conclude that re-estimation of model parameters and the use of local activity attribute distributions (frequency, start time and duration) is a desirable step in the transfer of the TASHA model from one context to another.
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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 | 0.016 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".