Understanding the relationships between regional accessibility travel behaviour and home values
Bibliographic record
Abstract
Accessibility is a comprehensive measure of the interaction between land use and transportation. It has been put forward as a performance-measure for transportation plans and projects; however its impact on individual travel activity and home location choice has been less studied. This research paper investigates the effect of accessibility on individual travel behavior, as well as accessibility’s monetary value in residential property markets. First, accessibility to jobs and workers by automobile is generated for the entire region. Second, the actual activity space of each household in the sample is computed. A linear regression model is used to predict the spatial dispersal and area of the household activity space, as well as the total distance traveled with the inverse balancing factors of the doubly constrained spatial interaction model of regional accessibility, while controlling for household characteristics. In addition, residents’ willingness to pay for higher levels of accessibility is measured through a hedonic regression model. The models show that households with high levels of accessibility to jobs have smaller, more spatially concentrated activity spaces as well as shorter travel distances than those with lower levels of regional accessibility. Accessibility also has a positive effect on home sale values. As a result, higher levels of accessibility promote shorter distances, more local travel patterns and are capitalized in home values. This study underscores the importance of using accessibility and activity spaces as performance-measures for land use and transportation planning. These measures will help transportation planners gain insight into travel behavior and travel demand and facilitate planning for more sustainable outcomes.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".