Drivers' Perceptions Towards Cyclists and Bikeshare Users in the ECOBICI Service Area
Bibliographic record
Abstract
Despite the rapid global motorization, especially in developing countries, the use of the bicycle as urban transportation has increased in the last 35 years (Shaheen, Guzman, & Zhang, 2012). However, the United States, Canada, and Mexico have low cycling levels with bicycle mode share of little more than one percent (Buehler & Pucher, 2012). Some of the possible alternatives to promote the use of the bicycle is that the presence of bikeshare systems can encourage cycling by providing a safer environment for all types of cyclists (Fischman & Schepers, 2014). This dissertation examines the drivers’ perception towards cyclists and the possible difference in perception towards Ecobici bikeshare users and private cyclists. This research was carried out in Mexico City, at the EcoBici bikeshare service area. Data collection was done by a self-reported survey distributed online and by intercept surveys conducted to drivers who drive within the study area and control area. The analysis of the 710 participants' responses shows that drivers from the control area have a more positive perception towards cyclists, especially on issues related to bicycle investment and bicycle infrastructure. Overall, younger generations reported a more positive perception towards cyclists, and most drivers perceive that cyclists are not predictable on the roads as most of the drivers reported feel nervous when overtaking cyclists. When comparing Ecobici users to private cyclists, the results suggest that drivers do not have a clear preference for Ecobici users over private cyclists. Nevertheless, drivers are also more in favor of encouraging family and friends to use Ecobici bicycles over private bicycles, which could indicate that, unconsciously, participants consider that traveling on an Ecobici bicycle is safer than going on a private bicycle. The results from this study could have an impact on policymakers and transportation practitioners in Mexico City who would like to improve drivers-cyclists’ interactions in the road and to promote the use of the bicycle for transportation.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".