Research on Nonlinear Associations and Interactions for Short-Distance Travel Mode Choice of Car Users
Bibliographic record
Abstract
Encouraging car users who travel short distances to shift from car mode to active travel modes can effectively alleviate urban traffic congestion and reduce carbon emissions. However, few studies have examined the determinants of the travel mode choice of short-distance car users and ignored the nonlinear associations and interactions between variables. This paper conducts a questionnaire survey to investigate the short-distance travel mode choice of car users who travel less than 4 km in a specific city. A random forest (RF) model is applied to examine the influence of key variables on these three travel mode choices of short-distance car users and to explore the nonlinear associations and interactions of the variables. Compared with multinomial logic model, the results of RF show that significant threshold effects exist in the relationship between the car user’s travel mode choice and the selected explanatory variables, mainly travel distance, road network density, distance to CBD, and number of bus stops. In particular, 1.2 km is a critical turning point for car and active travel mode choice, before which car users prefer to travel by walking and cycling and after which there is a significant increase in the car use probability. When the road network density was between 2.5 km/km2 and 6.5 km/km2, the proportion of car users who chose cycling showed an increasing trend, while car use showed a decreasing trend. These findings can provide a solid basis for planning managers to develop policy measures to encourage environmentally sustainable travel.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".