Exploring the Effect of Bicycle Infrastructure on Car Usage: A Case Study in Huhhot, China
Bibliographic record
Abstract
This study aimed to quantitatively investigate the effect of bicycle infrastructure on car usage. The mixed logit model with random coefficients was used to capture the differences in individuals’ preferences. Based on data from a stated preference survey conducted in Huhhot, China, the estimated results showed that the mixed logit model provides better fitting than the standard logit model. Considerable variations were found in individuals’ attitudes toward the use of cars and bicycles. Riding a bicycle is preferred by most individuals. Furthermore, based on the constraints for maintaining the effect on car usage equal, the equivalent change in parking fees for improvement in bicycle infrastructures was estimated. The results showed that the effect of a 100 m reduction in walking distance to bicycle stations on the probability of driving is the same as that of an approximately 2.00 yuan/h (US 0.30$/h) increase in the parking fees, and the effect of providing bike lanes is in line with additional parking fees of approximately 3.00 yuan/h (US 0.45$/h). The findings of this study can be an important reference for decision makers to consider improvements in bicycle systems and rational allocation of infrastructure investment and road resources.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".