The Influence of Origin Attributes on the Destination Choice of Discretionary Home-Based Walk Trips
Bibliographic record
Abstract
Walking has been recognized as an important mode of transportation in recent years, and recent research has improved travel demand models for walk trips. One important added stage is the distribution of walk trips, which can be evaluated using destination choice models. Previous studies have overlooked the importance of origin trip attributes in the destination choice of walk trips. With the aim of improving destination choice models for discretionary home-based walk trips, a questionnaire based on the previous day’s walk trips was used, and 422 trips were collected from individuals. A discrete choice logit model is used for discretionary trips by utilizing policy-related variables, such as origin-sensitive variables, land-use-related variables, and socio-economic conditions of individuals. Additionally, a solution is proposed to address the issue of data scarcity in considering the choice set. The results demonstrate that origin land-use (LU) variables, such as LU diversity index and access to green spaces, as well as socio-economic variables, like age and homeownership status, are statistically significant in the destination choice of discretionary home-based walk trips. One prominent result is that reducing the diversity of unattractive LU compared to increasing the diversity of attractive LU has a greater impact on the destination choice of such trips. Specifically, a 1% increase in the diversity of attractive LU in the origin area leads to a 0.031% increase in the probability of choosing a destination within that area, while a 1% decrease in the diversity of unattractive LU results in a 0.124% increase in the probability of choosing a destination within the area. The findings can be utilized in urban LU distribution and assessing their impact on destination choice for walk trips, ultimately informing future urban planning efforts in the context of pedestrian mobility.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".