Identifying Factors Contributing to the Motorcycle Crash Severity in Pakistan
Bibliographic record
Abstract
Motorcycle is a popular mode of transportation in many developing countries, including Pakistan. Since the last decade, the registered number of motorcycles in Pakistan has increased by six times, constituting 74% of the total registered vehicles. However, limited research efforts have been made to investigate motorcycle-related safety issues in Pakistan. Thus, the relationship between potential risk factors and injury outcomes of motorcycle crashes is still unclear in the country. This study, therefore, established a random parameter logit model to examine the factors associated with the motorcycle injury severity. The analysis is based on two years (2014–2015) of data collected through the road traffic injuries surveillance system from Karachi city, Pakistan. The results indicate that the summer season, weekends, nighttime, elderly riders, heavy vehicle, and single-vehicle collisions are positively associated with fatalities, while the presence of pillion passengers and motorcycle-to-motorcycle crashes are negatively associated with fatalities. More importantly, in the specific context of Pakistan, morning hours, young riders, and female pillion passengers whose clothes stuck in the wheel significantly increase the fatal injury outcomes. Based on the findings, potential countermeasures to improve motorcycle safety are discussed, such as strict enforcement to control motorcyclists' risky behavior and speeding, provision of exclusive motorcycles lanes, and education of female pillion passengers. The findings from this study would increase awareness of motorcycle safety and can be used by the policymakers to enhance road safety in Pakistan, as well as in other developing countries with similar situations.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".