Risk Perception of Vehicle-to-Vehicle Vendors and General Pedestrians: A Comparative Study
Bibliographic record
Abstract
Pedestrians account for 65% of all traffic fatalities worldwide. A sub-category of pedestrians is vehicle-to-vehicle vendors, who pose a concern in countries with rapid motorization. For example, 70% of traffic fatalities in Nigeria involve general pedestrians and vendors. Previous studies have highlighted vendors’ heterogeneous road crossing and car-following behaviors. Furthermore, they create a nuisance for general pedestrians. This study contrasts the risk perception of vehicle-to-vehicle vendors and general pedestrians and analyzes grouped and ungrouped illegal crossings of vendors. A questionnaire survey was developed, based on a literature review and expert knowledge, to identify variables associated with risk perception. Interviews based on a questionnaire were conducted in various locations in Dhaka, Bangladesh, which collected 1,019 responses containing information on the respondents’ demographic attributes, risk perception, aggressive behavior, near-crash experiences, and accepted yielding distances. Next, ordinal logit/probit and complementary log-log models were employed to analyze the data. The findings revealed that vehicle-to-vehicle vendors had a lower risk perception than general pedestrians. It also indicated that vendors would take a higher risk than general pedestrians. Furthermore, vendors jaywalking alone had a significantly lower perception of risk. Finally, gender, age, education, accepted yielding distance, and aggressive behavior were the most prominent factors affecting vendors’ risk perception. Gradually separating vendors from the traffic system by shifting them to proper street markets could be a critical solution.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".