The importance of neighborhood type dissonance in understanding the effect of the built environment on travel behavior
Bibliographic record
Abstract
For many years, researchers have struggled to separate the effects of personal tastes—including residential choices—from built environment and transport related factors when attempting to understand and model travel behavior. This paper will briefly describe how issues related to self-selection, if not controlled for in a travel behavior analysis, can lead to over- and under-estimation of the effect of the built environment on travel behavior. A theoretical model is presented, which is followed by an empirical analysis based on survey data capturing residential choice factors to test our theory. Our analysis shows that by separating people that have chosen their current home location based primarily on transport-related concerns from people who have located based primarily on housing and neighborhood characteristics, we are able to gain a nuanced understanding of how various “costs” associated with using public transit (access time, waiting time, and transfers) affect the likelihood of taking transit. We find a strong aversion to transfers as well as different responses to these factors based on reasons for living in a given location. We demonstrate how model predictions vary greatly especially when self-selection factors are included in the analysis. Findings from this research shed light on the importance of self-selection in travel behavior research, giving transport planners and engineers clear examples how ignoring these factors can lead to misleading findings.
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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".