Examining interaction effects among land-use policies to reduce household vehicle travel: An exploratory analysis
Bibliographic record
Abstract
Numerous studies have suggested that land-use policies can reduce vehicle travel through mode shifting and reduced trip lengths and generation of fewer or more efficient trips. The findings from previous studies also suggest that the combined effect of two or more land-use policies can be significant, although the effects of individual policies appear to be modest. These studies present area-wide impacts of land-use policies on travel and suggest that their effects are additive. However, very little is known about how each land-use policy interacts with the others at different levels of development intensity to reduce vehicle travel. In this study, we explore how three well-known land-use strategies (densification, mixed-use development, and street network improvement) interact with each other by testing possible combinations of land-use factors and focus on how these interactive effects vary by the level of development intensity. Employing ordinary least squares regression analysis using a dataset created for the Austin metropolitan statistical area (MSA) (using 2006 Austin Travel Survey data), we examine the impact of land use on household vehicle travel. Our findings suggest that interaction effects occur, but they vary by development intensity. The results of this study show the importance of considering both threshold (development intensity) and interaction (combination of policies) effects in understanding how land-use factors do and do not affect travel (based on their interactive opposed to only their direct and additive effects). Though this paper uses data from just one MSA and thus is merely suggestive, it does point to a possibly more nuanced use of the commonly prescribed planning and design policy variable to account for variation in effectiveness based on differences in development intensity. For example, we find that greater land-use intensification has higher efficacy in changing vehicle travel behavior in areas with relatively higher development intensity. Future research should include data from a broader array of metropolitan areas and incorporate additional predictor variables that were unavailable for this analysis.
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 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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".