Simultaneous Modeling of Endogenous Influence of Urban Form and Public Transit Accessibility on Distance Traveled
Bibliographic record
Abstract
This paper describes an attempt to understand better the endogenous relationships between urban form, accessibility to public transit, and daily travel distance. A model of two simultaneous equations was implemented. The model took into account the interaction between the ownership of vehicles and the choice of household location as explanatory endogenous variables for total distance traveled by respondents. Choice of household location was defined on the basis of cluster analysis (neighborhood typology) driven by land use mix, population density, and accessibility to transit. With socioeconomic variables controlled for, the impacts of neighborhood typologies combined with car ownership levels as endogenous choices were estimated with the use of a model with simultaneous equations. This research used data from the Quebec City, Quebec, Canada, origin–destination survey conducted in 2001. The data set included responses from more than 50,000 individuals. Among other results, the presence of endogeneity was confirmed. When endogeneity was not taken into account, the joint effects of car ownership and household location choices were underestimated. According to the model with simultaneous equations, the total distance traveled by individuals was primarily influenced by employment status and household structure. In fact, the total distance per individual had an average rate of growth of 50% when the individual was working full-time. The distance also increased by 5.7% per child and decreased by 2.4% per person. Although the elasticities of urban form and transit supply variables introduced individually into the model were small, the elasticities of neighborhood type as endogenous variables were much more relevant.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".