Representation of built environment and relationship to travel outcomes
Bibliographic record
Abstract
The built environment has been recognized as one of the major factors influencing travel behavior, but research into the specifics of how the built environment is represented is scant. This thesis investigates this relationship through different modes of representation. The first chapter offers an introduction to the topic, objectives, and research issues. In the second chapter, an original speed study is used to investigate the link between built environment and geometric design variables on vehicle operating speed along local roads. After controlling for traffic conditions, it was found that built form variables have a weak link with vehicle speed, geometric design being the main determinants. Chapter Three improves upon built form representation by using a neighborhood typology to simultaneously model household choice and GHG emissions. A lingering problem, however, is in the way in which land use mix is measured. Thus, the third chapter presents an entirely new method of measuring land use mix, which relies on interaction of complementary uses as opposed to proportion-based measurement. A preliminary evaluation of this method has proven that it is a great improvement on what is currently used to measure land use mix. The methodologies and case studies in this thesis demonstrate progress that has been made in how the built environment is represented in transportation studies, and it is hoped that they are a positive influence in the field.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".