Causes of Traffic Accidents: Implication to an Effective Traffic Management
Notice bibliographique
Résumé
The study sought to assess the causes of traffic accidents and its implication to an effective traffic management in Cagayan de Oro City. The independent variables were limited to equipment, roadway design, poor road maintenance, driver behavior, speed, improper loading, and unloading. The dependent variables narrowed to traffic incidents in Cagayan de Oro City. Data from the Police Traffic Unit is used to serve as respondents of the study. The data reveals that the majority of the causes of the accident were human error in a total of 4,370 cases for the year of 2014. Also human error in a total of 5,275 cases for the year of 2015, and still human error is the cause of accident for the first quarter of 2016 which have 1,796 cases. For the involvement of the collision, the data shows the highest case reported were in the car had 1,845 reports in the year of 2014. In 2015, the car had the greatest case reported in the involvement of the accident which had the same cases reported as 2014. And in the first quarter of 2016, a private vehicle had 1,349 cases reported for the involvement of accident in Cagayan de Oro City. For the common causes of the traffic accident, the data revealed that majority were in daytime visibility which had 4,492 in 2014, 5,536 in 2015 and 1,814 in the first quarter of 2016. For road condition, the majority of accident occurred were in the concrete of 2014 which had 4,492 cases, slippery in 2015 which had 5, 336 cases and the concrete in the first quarter of 2016 which had 1,335 cases reported. For weather condition, the majority were at a fair in 2014 which had 4,492 cases. In 2015 rainy were the dominant action which had 3, 453 cases and in the first quarter of 2016 fair condition which had 1,808 cases. For causes of traffic accident 4,370 cases for the year of 2014, also the human error in a total of 5,275 cases for the year of 2015, and still human error is the cause of the accident for the first quarter of 2016 which have 1,796 cases. For the places of the accident, the data revealed that Barangay Bulua is the most prone area of the accident which had 246 cases for the year of 2014. In 2015 data did not indicate where was the most prone area of an accident and for the first quarter of 2016, Barangay Bulua is the most prone area of an accident which had 61 cases reported K e ywords: Philippine National Police, Traffic Management, Ordinance, Law.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,003 | 0,002 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».