Access to Destinations: How Close Is Close Enough? Estimating Accurate Distance Decay Functions for Multiple Modes and Different Purposes
Bibliographic record
Abstract
Existing urban and suburban development patterns and the subsequent automobile dependence are \nleading to increased traffic congestion and air pollution. In response to the growing ills caused by urban \nsprawl, there has been an increased interest in creating more “livable” communities in which \ndestinations are brought closer to ones home or workplace (that is, achieving travel needs through land \nuse planning). While several reports suggest best practices for integrated land use-planning, little \nresearch has focused on examining detailed relationships between actual travel behavior and mean \ndistance to various services. For example, how far will pedestrians travel to access different types of \ndestinations? How to know if the “one quarter mile assumption” that is often bantered about is reliable? \nHow far will bicyclists travel to cycle on a bicycle only facility? How far do people drive for their \ncommon retail needs? \nTo examine these questions, this research makes use of available travel survey data for the Twin Cities \nregion. A primary outcome of this research is to examine different types of destinations and accurately \nand robustly estimate distance decay models for auto and non-auto travel modes, and also to comment \non its applicability for: (a) different types of travel, and (b) development of accessibility measures that \nincorporate this information.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".