Modeling Nonmotorized Travel Demand at Intersections in Calgary, Canada
Bibliographic record
Abstract
In September 2009, the City Council of Calgary, Canada, approved Plan It Calgary, which proposed policies that focused on the development of resilient neighborhoods through the intensification and diver-sification of urban activities around transit stations and routes. More intensive development and mixed land use encourage nonmotorized trips and reinforce comfortable, safe, and walkable streets. The development of high-density, mixed-use, and transit- and pedestrian-oriented communities has the potential to generate shorter trips to destinations; these trips are expected to result in a higher share of active travel modes, such as biking and walking. Thus, there is a growing need to estimate the impact of land use development scenarios and transportation policies on bicycle and pedestrian demand to predict nonmotorized trip volumes and design the related infrastructure adequately. In this study, on the basis of multiple linear and Poisson regression models were calibrated to estimate nonmotorized travel demand on the basis of geographic information system data, transportation services, and road characteristics. The empirical models developed in this research can be used to assess the impacts of urban design and built environments, such as development of high-density and mixed land use areas, of complete street construction in the middle ring communities of Calgary, and of influenced demand for active travel modes. The developed models also show the benefits of improved pedestrian infrastructure, such as improved network connectivity and increases in the length of pedestrian pathways, as well as the benefits of the integration of transit and walking modes and transit and bicycle modes in encouraging more nonmotorized travel demand. This method is a straightforward statistical analysis for practitioners, and the needed data are relatively easy to access.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".