EKSPLORASI MODEL TINGKAT KECELAKAAN LALU
(1) Jurusan Teknik Sipil, Fakultas Teknik, Universitas Negeri Semarang (UNNES
Abstract
Abstract: The objectives of the presents study was to explore mathematical models that could be used to estimate the number of accidents on interurban toll roads. Preditive models were developed by relating traffic exposure (average daily traffic and road section length) with the number of accidents per unit of time.Accident, roadway section and traffic volume data were obtained from Jagorawi, JakartaCikampek, padaleunyi, and palikanci toll road. Accident rate models were developed from those data using generalized linear modelling (GLM) techniques. The conclusions from the study were: (1) linier regression model was not appropriate to be used to predict accidents number, because accident occurence did not follow normal distribution, (2) Poisson regression possessed accident occurence characteristics: descrete, rare and random, and (3) Negative Binomial distribution was more appropriate to represent accident occurence phenomenon with overdispersion.
Keywords: accident rate, generalized linear modelling, Poisson regression, nagative binomial regression.
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Abstrak: Penelitian ini bertujuan untuk mengeksplorasi model matematis yang dapat digunakan untuk meramalkan jumlah kecelakaan, pada suatu ruas jalan tol antar kota. Model prediksi dikembangkan dengan menghubungkan paparan lalulintas, yang dinyatakan dengan volume lalulintas dan panjang ruas jalan, dengan keselamatan, yang dinyatakan dalam jumlah kecelakaan per satuan waktu. Data jalan, kecelakaan, dan lalulintas selama dua tahun, diambil dari jalan tol antar kota Jagorawi, Jakarta0Cikampek, Padaleunyi dan Palikanci. Model tingkat kecelakaan dikembangkan dari data tersebut dengan teknik generalized linear modelling (GLM), dan dikalibrasi dengan menggunakan teknik0teknik statistik. Beberapa kesimpulan yang dapat ditarik dari penelitian ini adalah: (1) Model regresi linier dengan distribusi normal tidak memadai untuk memprediksi kecelakaan; tingkat kecelakaan tidak terbukti mengikuti distribusi normal; (2) Penggunaan regresi Poisson lebih mencerminkan karakteristik kejadian kecelakaan: diskrit, acak, langka;   dan (3) Distribusi Binomial Negatif paling baik merepresentasikan kejadian kecelakaan dengan adanya gejala overdispersi pada data.
Kata kunci: tingkat kecelakaan, generalized linear modelling, regresi poisson, regresi binomial negatif.
Keywords
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