THE EFFECT OF TRAM MANAGEMENT ON ROAD TRAFFIC FLUIDITY AND ITS INFLUENCE ON DRIVERS’ BEHAVIORS AT A CRITICAL JUNCTION IN ALGERIA
Bibliographic record
Abstract
This research paper aims to study the effect of tram management on traffic fluidity and its impact on car drivers’ behaviors at junctions crossed by trams. The methodology used in this research is based on a mathematical model and an investigation of car drivers. The first step is to analyze the data of annual travelers’ attendance and assess the number of trams offered and needed in operation to respond adequately to the factual demand. The second step proceeds to show how the previous results of the trams’ fleet influence traffic jams. That is, this step identifies how the number of trams used in operation blocks other motorists and reduces traffic flow capacity at junctions. Finally, the purpose of the questionnaire is to determine car drivers’ opinions of the causes of traffic congestion at junctions and understand how this phenomenon affects their behaviors. The outcomes demonstrate that tram management is ineffective because there is a considerable gap between the annual offered tram fleet and the actual one needed according to the real statistical data. The high number of trams utilized is the leading cause of traffic congestion. Furthermore, this situation disturbs the control of traffic lights at common intersections. Unfortunately, this outcome is the main reason for drivers’ poor behavior, as 75.20% of car drivers are always stressed. These issues have intensified traffic jams in several junctions along the tram line. The article recommends some solutions to improve tram management and traffic fluidity to avoid the substandard behavior of car drivers at junctions.
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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 | 0.001 | 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".