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Record W4406960429 · doi:10.26760/jrh.v8i2.200-216

Kebijakan Pengembangan Infrastruktur dan Manajemen Umum Untuk Mengatasi Kemacetan di Kota Bandung

2024· article· id· W4406960429 on OpenAlexaff
Muhammad Aswal

Bibliographic record

VenueJurnal Rekayasa Hijau · 2024
Typearticle
Languageid
FieldEconomics, Econometrics and Finance
TopicEconomic Growth and Fiscal Policies
Canadian institutionsEncana (Canada)
Fundersnot available
KeywordsBusiness

Abstract

fetched live from OpenAlex

AbstrakKota Bandung berada diperingkat ke-14 kota termacet menurut survei Asian Development Bank (ADB). Salah satu penyebab adalah infrastruktur dan manajemen angkutan umum yang belum optimal. Policy paper ini bertujuan mengidentifikasi penyebab kemacetan, menilai infrastruktur dan sistem manajemen angkutan umum, serta memberikan rekomendasi kebijakan untuk mengoptimalkan infrastruktur dan meningkatkan investasi dalam transportasi umum.Metodologi yang digunakan mencakup analisis kualitatif dan kuantitatif. Dalam merumuskan permasalahan utama dengan metode 5 Why Analysis. Analisis kondisi transportasi menggunakan metoda furness, model trip assignment dan Volume Kapasitas Rasio. Sedangkan perumusan kebijakan dengan Metode SWOT dan Bardach's Eightfold Path. Policy paper berhasil merumuskan empat kebijakan utama, yaitu Peningkatan kinerja dan pengembangan transportasi jalan, Penerapan sistem transportasi cerdas, Pengembangan sistem transportasi yang terpadu dan terintegrasi, dan Mewujudkan SDM yang handal, profesional dan kompeten. Implementasi kebijakan ini diharapkan meningkatkan aksesibilitas, mengurangi kemacetan, serta menciptakan sistem transportasi yang efektif dan efisien.Kata Kunci: kemacetan, Angkutan Umum, Sistem transportasi, Pengembangan Infrastruktur. AbstractBandung is ranked 14th as the most congested city according to a survey done by the Asian Development Bank (ADB). One of the main causes is infrastructure and public transportation management that is not optimal. This policy paper intend to identify the reason behind this congestion, assessing the infrastructure and public transportation management systems, and provide policy recommendations to optimize the infrastructure and increase investments in public transportation.The methodology used includes both qualitative, and quantitative analysis. The main issues are formulated by using the 5 Why Analysis. Analysis for the transportation condition uses the furness method, trip assignment models, and Volume Capacity Ratio (VCR). Policy formulation employs SWOT Analysis and Bardach's Eightfold Path. This policy paper had successfully formulates four main policies which includes improving the performance, and development of road transportation, implementing intelligent transportation system, developing an integrated transportation system, and establishing competent, professional, and skilled human resources. The implementation of these policies is expected to improve accessibility, reduce congestion, and develop an effective and efficient transportation system.Keywords: Congestion, Public Transport, Transportation System, Infrastructure Development.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.059
Threshold uncertainty score0.199

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.003
Science and technology studies0.0030.002
Scholarly communication0.0100.005
Open science0.0010.004
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0590.008

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.

Opus teacher head0.024
GPT teacher head0.233
Teacher spread0.209 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2024
Admission routes1
Has abstractyes

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