Characteristics, Roles and Challenges of Traffic Personnel: Implications toward Efficient Traffic Management System
Bibliographic record
Abstract
The purpose of this study was to determine the characteristics, roles and challenges of traffic personnel and their implications toward efficient traffic management system in Bacolod City during the second quarter of calendar year 2018. A mixed methods research design was used which involved the use of both quantitative and qualitative methods by means of survey responded by 150 traffic personnel, key informant interview participated by 3 Barangay Captains and a City Councilor, and focus group discussion participated by 6 traffic personnel which were all selected through a purposive and convenience sampling techniques. Frequency count, percentage, weighted mean, standard deviation, Mann- Whitney U, Kruskal Wallis and IBM SPSS Version 19 were employed to analyze and present the data for quantitative part. While the qualitative part of the study, Thematic Analysis was utilized. The findings showed that traffic personnel who participated in the study were almost equally divided when grouped according to age, while majority were male, attained college level, have less than 7 years of experience and designated as traffic enforcer. Meanwhile, not all completed the required trainings. When it comes to their roles as traffic personnel, it showed that they are mainly managing traffic flow and implementing traffic rules and regulations in the roads. Moreover, it showed that majority of them are highly knowledgeable on City Ordinance 338, and there are no significant differences when they were grouped according to age, sex, educational attainment, and job designation. However, significant differences were found in their level of knowledge on the aforementioned ordinance when they were grouped according to length of service and trainings attended. On the other hand, it was found out that the top most challenge experienced by the participants is the arrogance of drivers. The lack of discipline which includes disregarding of traffic rules and regulations among drivers follows next. Ignorance of the traffic rules and regulations among road users, attitude of drivers, bad weather conditions, high volume of vehicles and road widening projects are also included in the short list of challenges encountered by traffic personnel in the City. Finally, results of this study were used in formulating an enhanced traffic management system program for Bacolod Traffic Authority Office.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".