Predicting the Collisions of Heavy Vehicle Drivers in Iran by Investigating the Effective Human Factors
Bibliographic record
Abstract
Traffic collisions are one of the most important challenges threatening the general health of the world. Iran’s crash statistics demonstrate that approximately 16,500 people lose their lives every year due to road collisions. According to the traffic police of Iran, heavy vehicles (including trailers, trucks, and panel trucks) contributed to 20.5% of the fatal road traffic collisions in the year 2013. This highlights the need for devoting special attention to heavy vehicle drivers to further explore their driving characteristics. In this research, the effect of heavy vehicle drivers’ behavior on at-fault collisions over three years has been investigated with an innovative approach of structural equation modeling (SEM) and Bayesian Network (BN). The database utilized in this research was collected using a questionnaire. For this purpose, 474 heavy vehicle drivers have been questioned in the Parviz Khan Border Market, located on the border of Iran and Iraq. The response rate of the survey was 80%. The participants answered the questions on Driver Behavior Questionnaire (DBQ) and a sleep assessing questionnaire named Global Dissatisfaction with Sleep (GSD). In this research, human factors affecting at-fault collisions of heavy vehicles were identified and their relationships with other variables were determined using the SEM approach. Then the descriptive model constructed by the SEM method was used as the basis of the BN, and the conditional probabilities of each node in the BN were calculated by the database collected by the field survey. SEM indicates that other attributes including GSD, mobile usage, daily fatigue, exposure, and education level have an indirect relation with heavy vehicle drivers’ at-fault collisions. According to the BN, if there is no information about the characteristics of a heavy vehicle driver, the driver will likely have at least one collision during the next three years with the probability of 0.17. Also, it was indicated that the minimum probability of the at-fault collision occurrence for a heavy vehicle is 0.08.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".