Control and prediction of traffic crashes in the residential streets in Iraq using the expert system
Bibliographic record
Abstract
Residential streets suffer from various traffic safety problems related to traffic accidents, especially in low and middle-income countries. This research's objective was to develop a novel system for controlling and predicting traffic accidents in the residential streets using the expert system (CPTCRSI-ES). Knowledge of the CPTCRSI-ES was collected from domain experts and published sources. The system comprised nine modules: the module to identify types of car accidents–causes-preventive actions effects, module to identify safety problems and related solutions, module to find the speed calculation, module to find the cut-through traffic calculation, module to find the Equivalent Property Damage Only (EPDO), module to find residential density calculation, module to rank traffic safety parameters, module to control the traffic accidents, and module to find the traffic accidents prediction. Verification, validation, and evaluation process (V, V and E) was conducted. Results were statistically analyzed using Number Cruncher Statistical Systems (NCSS) version 26. Verification was done by asking three groups of experts involving 20 professional engineering computers and domain experts. The arithmetic mean for evaluators' responses was higher than 4.2 out of 5, indicating a strong agreement. The Cronbach's alpha was 0.960, and internal consistency reliability (ICR) showed excellent reliability. Results for verification demonstrated the satisfaction of the experts with the proposed system. In the validation process, the experts were requested to propose appropriate strategies and solutions to address the safety problems and reduce traffic accidents. The arithmetic mean for matching the experts' answers and the outputs of CPTCRSI-ES was higher than 4, and the Cronbach's alpha was 0.917 and ICR, indicating excellent reliability. Finally, evaluation of the system by its end-users showed that the overall assessment rating was more than 4, Cronbach's alpha was 0.932, and ICR showed excellent reliability. Results for V, V, and E demonstrated that the system had met its primary objectives. The proposed system adopted in this research can help traffic safety authorities control and predict traffic accidents.
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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) | 0.008 | 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".