Structural Equations Model of Land Use Patterns, Location Choice, and Travel Behavior in Southern California
Bibliographic record
Abstract
This paper continues a series of papers that address the relationship between travel behavior and land use patterns under a structural equations modeling framework in different contexts for comparative purposes. The proposed model structure in this paper is, by design, heavily influenced by a model developed for Lisbon, Portugal; Seattle, Washington; and Montreal, Canada, in addition to a revisited model for Lisbon that used more recent data. In all previous models, significant effects of land use patterns on travel behavior were found. The variables included in this and past models are multidimensional and include short-term decisions (number of trips by mode and trip scheduling) and long-term decisions (home location, car ownership, and mobility). The modeled land use variables measure the levels of urban intensity, density, diversity, and accessibility. The land use patterns are described at the residence and employment zones. To account explicitly for the self-selection bias, the land use variables are modeled explicitly as functions of the socioeconomic attributes of individuals and their households. The findings from Los Angeles, California, and the surrounding metropolitan region are presented in this paper and then compared with those from Lisbon, Seattle, and Montreal. The results show that, similarly to the other cases, land use patterns influence travel behavior significantly. Other commonalities were also found in all four environments, as were some important differences.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".