Safety Risk of Nonmotorized Vehicles from the Perspective of Motorized Vehicle Drivers
Bibliographic record
Abstract
Collisions with motorized vehicles (MVs) are one of the leading causes of nonmotorized vehicle (NMV) crashes in a heterogeneous traffic stream. As well as NMVs’ inherent vulnerability, MV drivers’ risk perceptions of NMVs may also influence MV–NMV crashes. However, until now, this subjective perception has been little explored in the literature. This study examines the potential impact of numerous factors associated with motorized road users’ perception of risk and the operational aspects of NMVs on MV–NMV crashes. An ordered probit model was developed using self-reported data from 1,560 Dhaka city motorists (motorcyclists, and car and bus drivers). Findings revealed that motorists have a higher probability of becoming aggressive, deem NMV drivers’ behavior to be risky, and have low positive attitudes toward such vehicles when they have a stronger MV–NMV crash history. The results also suggest that bus drivers have fewer crashes with NMVs, although they feel these vehicles are structurally unsafe. In addition, age, education, and perceptions of lane separation, movement, stops, and users’ trip frequency were significant in predicting crash frequency. Further, older and illiterate drivers were more likely to be involved in collisions with NMVs. Because of the bias of self-reported data, analysis of variance tests were conducted, and the results demonstrated a significant difference in risk perceptions between motorcyclists, car drivers, and bus drivers. Risk perceptions of NMVs were the highest among motorcyclists. The findings of this study are expected to aid policymakers in improving motorists’ perceptions of NMVs and in increasing the latter’s safety in developing nations with heterogenous traffic systems.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".