Importance of Automobile Mode Share in Understanding the Full Impact of Urban Form on Work-Based Vehicle Distance Traveled
Bibliographic record
Abstract
Vehicle kilometers traveled (VKT) has been widely used in regional planning as a key sustainability performance indicator. Many regional growth plans for reducing work trip VKT have been proposed, with a focus on land use development in employment centers. Despite the potential impact of urban form on the reduction of VKT, the fundamentals of how this takes place remain unclear. This study analyzes the relationship between urban form, VKT, and mode shares by examining office commuting patterns in the Greater Toronto and Hamilton Area (GTHA) through a structural equation modeling approach. The model supports the substantial impact of urban form on the reduction of VKT; however, it indicates that such an impact is made mostly through shifting modes, rather than directly on reduced travel distances. This model is then used to evaluate critically a regional growth plan for the GTHA, finding that strategies focusing solely on increasing land use densities in employment centers are not likely to reduce regional VKT significantly without also easing commuting auto dependency. Thus, it is recommended that more sustainable travel alternatives for workers in employment centers should be provided to achieve a sufficient reduction in VKT.
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 | 0.000 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".