Joint Modelling of Propensity and Distance for Walking Trip Generation
Bibliographic record
Abstract
The paper presents an econometric investigation on walking trip generation for work and school activities. It uses a joint econometric modelling approach and highlights the importance of considering walking distance jointly with walking propensity in the walking trip generation model. Unlike any motorized modes, walking requires energy from the traveller him/herself and hence the total amount of walking distance has limiting effects on the walking trip making behaviour. The paper develops the joint econometric model for the decision to make non-zero walking trips, walking propensity and walking distance. Empirical models are estimated using large scale household travel survey data collected in the Greater Toronto and Hamilton Area (GTHA) in 1996, 2001 and 2006. Same models are estimated for three years and the results are compared. Empirical models clearly validate the proposal that for walking, travel distance should be considered jointly with propensity. It is also found that aggregate land use and population characteristics influence the decision to make non-zero walking trips more than they influence the walking propensity and distance. The relationship between auto ownership and walking trip generation is proved to be very strong; however, zonal average auto ownership has higher impact than the traveller’s own household auto ownership. By comparing the empirical models, it is clear that although there has been a slight improvement on baseline tendency to consider walking for work or school trips, the baseline walking propensity and distance remain unchanged over the years in the GTHA. Hence, in addition to land use policies, more rigorous applications of public education and social marketing would be required to encourage greater use of walking for work or school activities.
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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 | 0.002 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 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 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".