TINGKAT PELAYANAN RUAS JALAN TEUKU UMAR DAN JALAN SETIABUDI KOTA SEMARANG DI TINJAU DARI ASPEK PERMASALAHAN KEMACETAN LALU LINTAS
Bibliographic record
Abstract
Traffic congestion is a classic problem in big cities especially in developing countries such as Indonesia. Many things can be the cause of the traffic jam, for it is necessary to research on traffic congestion as much as possible, with the hope of producing the best solution for all.Congestion that occurs due to the activity and mixing between local and regional flows. The purpose of this study is to analyze the level of service and performance road cut Teuku Umar street and road Setiabudi, so it can be arranged alternative actions that can be done to address the problem of traffic congestion.The method used in this research is by using Quantitative Deductive Method Rationalistic, with retrofitting Level of Service and analysis of motion control (maneuver) in order to see the level of service road and the movement of traffic in motion over Jatingaleh Region Semarang. The results of this study is the identification of the causes of traffic congestion and the suitability of the performance in the study area (Region Jatingaleh).Level of service for road Jatingaleh area is the level of F means that hampered the flow of traffic, low speed, volume over capacity, congestion often occurs at a time long enough so that it can drop to zero. In the piece Jalan Teuku Umar and Jalan Setiabudi there are several types of motion control, there are approximately 5 crossing, diverging 6, 7 merging and 3 weaving. Seeing the condition of the poor level of service in most of the observation point, of course, reduce the performance of the maneuver crossing the road.Need for the recommendation that a new path with the added solution of the motion control analysis (maneuver) in the form of Grade Separation, can be Overpass (Flyover) or underpass.Keywords: transportation, congestion, road, and traffic.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
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".