Peningkatan Keselamatan Pada Simpang Dengan Menerapkan RHK Sepeda Motor (Studi Kasus Simpang Empat Bersinyal Srikandi Di Kabupaten Pasuruan)
Bibliographic record
Abstract
Pasuruan Regency is one of the regions in Indonesia which is precisely located in the province of East Java. With a very rapid economic development will certainly affect the flow of traffic, especially at the intersection. One of the most populous intersections is the Srikandi Four Intersection which is located in Pandaan District. To improve safety at an intersection, traffic management is necessary. This study aims to analyze the performance of the intersection by using the MKJI calculation and calculate the area of the Special Stop Room (RHK) of a motorcycle that refers to the Guidelines for Designing Motorcycle RHK at a Signed Intersection in the Urban Area planned on Jalan R.A. Kartini and Jalan A. Yani. The method used to plan the RHK of this motorcycle uses quantitative descriptive methods and qualitative descriptive methods. Intersection performance results obtained from calculations for the existing conditions of the North approach capacity (Jalan Urip Sumoharjo) 252 pcu / hour, queue length 131 m, degree of saturation of 0.85. Eastern approach capacity (Jalan Pahlawan Sunaryo) 265 pcu / hour, queue length 146 m, degree of saturation 0,85. South approach capacity (Jalan R.A. Kartini) 579 pcu / hour, queue length 134 m, degree of saturation of 0.85. The Western approach capacity (Jalan A. Yani) is 730 pcu / hour, the queue length is 104 m, the degree of saturation is 0.85, while the average delay is 59.10 seconds / pcu. From the performance analysis of the intersection, the length of the RHK motorcycle for the R.A. Kartini is 11.5 meters long, while for Jalan A. Yani it is 10.3 meters long.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".