Developing CUSTOM framework: explore telecommuting-induced activity-travel demands with mode choice
Bibliographic record
Abstract
This paper presents an econometric framework of jointly modelling daily activity scheduling (activity type, time expenditure, and location choices known as the CUSTOM system) and travel mode choice considering Random Utility Maximization (RUM) behaviour. The joint model is applied to model workers’ daily activity-travel demand with flexible work arrangement choices. The joint framework is flexible to capture workers’ activity-travel patterns under different workplace (telecommuting, not telecommuting, or hybrid) arrangement options. The model is empirically estimated using datasets collected in the Greater Toronto Area (GTA) in 2021. The analysis explores how different workplace arrangements affect activity-travel demand. The model is calibrated to simulate scenarios where the distribution of work-from-home workers varied between the levels observed from 2016 to 2021. The scenario analysis validates the behavioural predictability of the joint CUSTOM system. This highlights the potential of the agent-based activity-based modelling system to forecast the influence of disruptive events on travel behaviours.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".