Disaggregate Probabilistic Models to Predict Trip Generation Propensities and Mode Choice Behaviour
Bibliographic record
Abstract
Travel behaviour models serve as important tools to understand what factors affect trip generation and preferred mode of travel in different contexts.These models can be used to quantify the impacts and assess the consequences of development plans and policy actions.Growing populations and the increase in travel demand warrant investigation into determinants of travel behaviour that can guide policy changes and infrastructure investments.This thesis uses two cross-sectional datasets containing various sociodemographic and land-use attributes from 2015 and 2019.Trip-generation propensities for transit and automobile modes are predicted using a bivariate ordered probit approach which enables the determining of factors affecting the trip-generation propensity of each mode while establishing the correlation between their propensities.Further, a multinomial logit model is estimated to investigate the determinants of mode choice for home-based discretionary trips.The results show that improvement in areas with low transit accessibility can considerably increase the transit trip-making propensity and living in downtown Toronto increases the probability of choosing active modes of travel.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".