Chasing Sustainability
Bibliographic record
Abstract
One of the solutions suggested for mitigating the detrimental effect of motor vehicles on society is to implement transit-oriented development (TOD). This type of development is intended to reduce automobile use and urban sprawl as well as to provide communities with more socially, environmentally, and economically sustainable neighborhoods that offer a variety of mobility choices. This study attempted to find out whether new residents adopted more sustainable modes of transportation after their relocation to a TOD. The analysis determined which factors influenced travel mode switching decisions by specifying a multilevel multinomial logistic regression model. Data for the analysis were drawn from a travel behavior survey conducted on residents in seven North American TODs in 2013. The results showed that newcomers adopted more sustainable travel modes for amenities and leisure trips after they relocated to a TOD but that they were less likely to do so for work and shopping trips. To encourage more sustainable travel modes, the study findings suggested that transit incentives coupled with workplace parking charges needed to be considered. Factors that were found to increase the probability that new TOD residents would switch to a more sustainable mode of transportation included their awareness of the environmental impact of each travel mode, the ease with which it was possible to walk through the neighborhood and to various destinations, and the proximity to transit stops. However, larger household size, homeownership, and the addition of a new car had negative impacts. The findings provided new insights into TOD planning and its link to travel behavior; these insights could benefit planners, engineers, and policy makers who have adopted the TOD approach to development with the goal of mitigating car usage.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.015 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.001 | 0.015 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.035 | 0.007 |
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".