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PENGEMBANGAN MODEL KESELAMATAN UNTUK PENYEBERANGAN JALAN KOTA BANDUNG (STUDI KASUS SIMPANG BERSINYAL) Istiqomah Nurhidayati; Aine Kusumawati; Sri Hendarto
Jurnal Ilmiah Teknologi Infomasi Terapan Vol. 5 No. 2 (2019)
Publisher : Universitas Widyatama

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1094.366 KB) | DOI: 10.33197/jitter.vol5.iss2.2019.279

Abstract

Traffic crashes are the 10th main cause of death in Bandung. Pedestrians are road users who are more vulnerable to crashes. Pedestrian crashes often occur at intersections as diverseroad users move in the same road space, thus making interaction between road users more complex. Currently, pedestrian safety at intersection is still very low, so it is important to do a study to reduce the number of pedestrian crashes. A model that has been developed by FHWA (Federal Highway Adminstration) for pedestrian safety at intersection is PedISI (Pedestrian Intersection Safety Index). This model can be used to determine which intersections need to be addressed before a crash occurs based on PedISI values. PedISI values show the level of pedestrian safety at intersection. This thesis develops Pedestrian Intersection Safety Index (PedISI) model for signalized intersection in Bandung. The data used consist of pedestrian crashes data, intersection characteristic data, behavioral(conflicts) data and safety rating data. Data were collected from 16 signalized intersections of types 8/2 D, 6/2 D, 4/2 D, 4/2 UD and 2/2 UD. PedISI model formed for signalized intersection in Bandung is PedISI = 3,184 + 0,032THRULNS - 0,005MEDIAN + 0,001SPEED85th with R2 = 0,525. PedISI values for signalized intersections in Bandung based on the model is 3,24 ? 3,45. The average value of PedISI is 3,35 with a standard deviation of 0,06. This model is able to describe the safety conditions of intersection though it has small variation in PedISI values. This is due to the results of surveys that indicated uniformity in respondents perceptions of safety rating values. Thus, it is necessary to improve the safety data collection techniques to obtain a better model.
ANALISIS SKALA PRIORITAS PENANGANAN UJI KELAIKAN FUNGSI JALAN DITINJAU DARI ASPEK TEKNIS PENDEKATAN KUANTITATIF ANALYTIC HIERARCHY PROCESS (STUDI KASUS: JALAN LINTAS SUMATERA TANJAKAN TARAHAN) Catur Marina, Bernaditha; Hermon Frederik Tambunan; Sri Hendarto; Jimmael Martogi Hutagaol
Nusantara Hasana Journal Vol. 5 No. 1 (2025): Nusantara Hasana Journal, June 2025
Publisher : Yayasan Nusantara Hasana Berdikari

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59003/nhj.v5i1.1494

Abstract

The rapid population growth in Bandar Lampung has led to an increase in vehicle ownership, which necessitates the provision of adequate road infrastructure and facilities. According to the Regulation of the Minister of Public Works No. 11/PRT/M/2010 concerning Guidelines and Requirements for Road Function Feasibility, road functionality is classified into three categories: Feasible (LF), Conditionally Feasible (LS), and Not Feasible (TLF). To determine the appropriate category of road functionality, the Analytic Hierarchy Process (AHP) method is employed. AHP is a decision support model involving selected respondents who are knowledgeable about the assessed road segment and the criteria for road feasibility. The respondents in this study include representatives from road authorities (Ministry of Public Works and Housing), traffic authorities (Indonesian National Police), transportation agencies (Ministry of Transportation), as well as members of the public, students, and academics. Based on the analysis, the AHP-LFJ score for the Kalianda–Tarahan road segment is 0.696 (approximately 69%), which corresponds to the category of Conditionally Feasible (LS) according to the AHP-LFJ classification thresholds: LF (100%), LS (0.99–0.67), LT (0.66–0.33), and TLF (≤0.32). Roads categorized as Conditionally Feasible (LS) meet part of the technical requirements and are still considered safe for road users.
PENGARUH PERSENTASE SEPEDA MOTOR DALAM VOLUME LALU LINTAS TERHADAP INDEKS KEBISINGAN LALU LINTAS (STUDI KASUS: RUAS JALAN DENGAN PEMISAH LAJUR DI JALAN SOEKARNO HATTA KOTA BANDUNG) Linlin Linow Linlin; Sri Hendarto; Didin Kusdian; Chandra Afriade
Jurnal Media Teknologi Vol. 13 No. 1 (2026): Jurnal Media Teknologi
Publisher : Fakultas Teknik UNIGAL

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25157/jmt.v13i1.6256

Abstract

Penelitian ini bertujuan untuk menganalisis pengaruh persentase sepeda motor (%SM) dalam volume/arus lalu lintas dan faktor distribusi arus terhadap Indeks Kebisingan Lalu Lintas/Traffic Noise Index (TNI) menggunakan Metode Regresi Linier Berganda dengan pendekatan Kuantitatif menggunakan data hasil pengukuran Sound Pressure Level (SPL) (dB) menggunakan Sound Level Meter (SLM) untuk kemudian dikonversi ke skala energi linier menjadi Kebisingan Ekuivalen (Leq) (dB) dan perekaman Volume Kendaraan mengacu pada PKJI 2023. Hasil analisis menunjukkan bahwa variabel tersebut secara simultan dan parsial berpengaruh signifikan terhadap Indeks Kebisingan Lalu Lintas/Traffic Noise Index (TNI). Distribusi arus lalu lintas yang tidak seimbang (nilai distribusi tinggi) cenderung meningkatkan kebisingan, sementara distribusi yang stabil atau merata menurunkan tingkat kebisingan. Koefisien regresi variabel Persentase Sepeda Motor (%SM) yang dihasilkan memiliki nilai yang sama untuk kedua arah (arah ke Barat dan arah ke Timur). Hal tersebut menunjukan besarnya pengaruh persentase Sepeda Motor (%SM) terhadap peningkatan Traffic Noise Index (TNI), artinya setiap peningkatan 1% sepeda motor dari total arus/volume kendaraan meningkatkan Traffic Noise Index (TNI) sebesar 0.029 dB. Hal tersebut sesuai dengan konsep TNI yang peka terhadap fluktuasi/Noise Climate, dimana Lalu lintas motor biasanya menghasilkan puncak-puncak kebisingan yang meningkatkan selisih antara L10 & L90 (NC = L10-L90). Persamaan Model Regresi : Y = 81.874 + 0.001X1 – 0.029X2 + 3.834X13 dimana: Y = Traffic Noise Index (TNI) (dB), X1= Volume Lalu Lintas Total (Kendaraan/Jam), X2=% Sepeda Motor (SM), X3=Faktor Distribusi Arus, yang mengindikasikan adanya pengaruh lintas arah (spasial) terhadap kebisingan. Indikasi tersebut menunjukkan bahwa arus dari satu arah dapat memengaruhi kondisi kebisingan di arah berlawanan. Secara keseluruhan, model matematis ini dapat dijadikan dasar bagi perumusan strategi pengendalian kebisingan lalu lintas berbasis pada komposisi kendaraan dan distribusi arus lalu lintas dari 2 arah.