What Affects Safety Perception of Female Ride-Hailing Passengers? An Empirical Study in China Context
Bibliographic record
Abstract
Ride-hailing services provide an efficient way to travel, but they also cause some safety incidents, which make female passengers uneasy. Analyzing the safety perceptions of female passengers, particularly their psychological and emotional responses, can assist operators in developing effective solutions and safe travel environments for them. This study explores factors that are likely to affect the safety perception of female ride-hailing passengers using a subjective method (data was obtained from 596 Chinese female passengers). The methodologies adopted mainly include confirmatory factor analysis (CFA) and a maximum-likelihood structural equation model (ML-SEM). A passenger safety perception model is developed by considering various elements such as safety expectation, platform trust, perceived environment, and safety awareness. The results revealed that safety perception is positively influenced by perceived environment and safety expectation (containing three subdimensions, namely, driver behavior, traveling together, and mobile phone dependence). The effects of safety awareness and platform trust on safety perception are mediated by perceived environment and safety expectation, respectively. Regarding overall effects, safety expectation is the most powerful predictor for safety perception of female ride-hailing passengers, followed by platform trust, perceived environment, and safety awareness. Finally, countermeasures are offered from the perspectives of operators, drivers, and passengers to enhance the safety perception of female ride-hailing passengers. A high level of ride-hailing safety would undoubtedly boost the female passenger’s trust and consequently ridership.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".