STUDI GELOMBANG KEJUT PADA SIMPANG BERSINYAL DENGAN MENGGUNAKAN EMP ATAS DASAR ANALISIS HEADWAY (Studi Kasus Pada Simpang Bersinyal Jalan Raya Wonogiri-Sukoharjo – Jalan Gedongan – Jalan Ciu Karangwuni)
Bibliographic record
Abstract
Simpang bersinyal Jalan Raya Wonogiri-Sukoharj – Jalan Ciu Karangwuni – Jalan Gedongan merupakan salah satu simpang bersinyal 3 fase yang ada di Kabupaten Sukoharjo yang sering mengalami kemacetan pada jam sibuk, khususnya pada pendekat simpang Jalan Raya Wonogiri-Sukoharjo Selatan. Untuk itu dilakukan studi gelombang kejut di pendekat simpang Jalan Raya Wonogiri-Sukoharjo Selatan menggunakan nilai EMP dengan dasar analisis headway. Penelitian dilakukan pada hari Kamis, 18 Oktober 2018 pada jam puncak pagi jam 05.30-08.00 WIB. Analisis Headway menghasilkan nilai EMP MC= 0,45 dan HV= 1,29 yang selanjutnya nilai tersebut digunakan untuk merubah jumlah kendaraan menjadi satuan mobil penumpang (smp). Langkah selanjutnya adalah mencari hubungan matematis antara arus, kecepatan dan kepadatan menggunakan model greenshield, yang menghasilkan kecepatan arus bebas (Sff), kepadatan saat macet (Dj), dan Jumlah kendaraan maksimal (Vm). Hasil-hasil tersebut digunakan untuk menghitung nilai gelombang kejut dengan nilai tertinggi yang terjadi pada pendekat simpang Jl. Raya Wonogiri-Sukoharjo Selatan Lajur Luar dengan nilai ωab= -1,42 km/jam, ωcb= -13,75 km/jam, ωac= 12,15 km/jam. Nilai gelombang kejut tersebut digunakan untuk menghitung waktu penormalan dan panjang antrian pada masing-masing pendekat simpang.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.048 | 0.008 |
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".