Aplikasi Model Rantai Markov Dalam Pengelolaan Jalan di Kabupaten Bangka Barat

Ahmad Sazali; Bagus Hario Setiadji; Bambang Haryadi

doi:10.21107/rekayasa.v12i2.5907

Aplikasi Model Rantai Markov Dalam Pengelolaan Jalan di Kabupaten Bangka Barat

Ahmad Sazali, Bagus Hario Setiadji, Bambang Haryadi

Abstract

Pemodelan kinerja perkerasan jalan sangat dibutuhkan dalam upaya mendapatkan gambaran informasi perubahan kondisi perkerasan jalan di masa mendatang. Penelitian ini fokus pada prediksi kondisi perkerasan jalan menggunakan metode rantai Markov dengan cara melakukan perkalian antara Matriks Probabilitas Transisi (MPT) dengan vektor kondisi awal. Data utama yang digunakan dalam pengembangan model ini adalah data kondisi perkerasan dan data histori penanganan jalan tahun 2016 – 2017. Aplikasi model rantai Markov pada jaringan jalan di Kabupaten Bangka Barat untuk periode lima tahun (2018 – 2022) dengan asumsi dilakukan penanganan untuk seluruh ruas jalan setiap tahun sesuai dengan jenis program penanganan (pemeliharaan rutin, pemeliharaan berkala, rehabilitasi dan rekonstruksi) berdasarkan batasan persentase kondisi kerusakan jalan. Gambaran perubahan kondisi jaringan jalan kabupaten yang dihasilkan dari penerapan model tersebut cukup optimal, dimana pada tingkat kondisi Baik (B) terus mengalami peningkatan. Sebaliknya, pada tingkat kondisi Rusak Berat (RB) terus mengalami penurunan setiap tahunnya. Hasil penelitian ini diharapkan dapat menjadi acuan bagi pengelola jalan dalam rangka perencanaan pengelolaan jaringan jalan.

Application of Markov Chain Model In Road Management in Bangka Barat Regency

ABSTRACT

Road pavement performance modeling is needed to get an overview of information about changes in road pavement conditions in the future. This research focuses on the prediction of road pavement conditions using the Markov chain method by multiplying the Transition Probability Matrix (TPM) with the initial condition vector. The main data used in the development of this model are pavement condition data and road handling history data for 2016-2017. Application of the Markov chain model in the road network in West Bangka Regency for a period of five years (2018 - 2022) with the assumption that handling is done for all roads every year according to the type of treatment program (routine maintenance, periodic maintenance, rehabilitation, and reconstruction) based on the percentage limit of road damage conditions. The description of changes in district road network conditions resulting from the application of the model is quite optimal, were at the level of good condition (B) continues to increase. Conversely, the level of severely damaged (RB) condition continues to decline every year. The results of this study are expected to be a reference for road managers in the framework of road network management planning.

Keywords : Road pavement performance; Markov chain; Road network management

Keywords

Kinerja perkerasan jalan; Rantai Markov; Pengelolaan jaringan jalan

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References

Abaza, K. A. (2016). Simplified staged-homogenous Markov model for flexible pavement performance prediction. Road Materials and Pavement Design, 17(2), 365–381. https://doi.org/10.1080/14680629.2015.1083464

Abaza, K. A., & Ashur, S. A. (1999). Optimum Decision Policy for Management of Pavement Maintenance and Rehabilitation Optimum Decision Policy for Management of Pavement, (February 2015). https://doi.org/10.3141/1655-02

Abaza, K. A., Ashur, S. A., & Al-Khatib, I. A. (2004). Integrated Pavement Management System with a Markovian Prediction Model. Journal of Transportation Engineering, 130. https://doi.org/10.1061/(ASCE)0733-947X(2004)130

Arimbi, G. (2015). Network-Level Pavement Performance Prediction Modelling with Markov Chains (Predicting the Condition of Road Network For Rijkswaterstaat). Delf University of Technology.

Cahyaningrum, E. K. (2014). Pemodelan Estimasi Kerusakan Perkerasan Jalan (Studi Kasus Ruas Jalan Nasional di Wilayah Kerja PPK 3, Satker PJN Wilayah Propinsi DIY). Universitas Gajah Mada.

Departemen Pekerjaan Umum. (2005). Teknik Pengelolaan Jalan: Seri Panduan Pemeliharaan Jalan Kabupaten. Bandung: Departemen Pekerjaan Umum.

Durango, P. L., & Madanat, S. M. (2002). Optimal maintenance and repair policies in infrastructure management under uncertain facility deterioration rates : an adaptive control approach, 36, 763–778.

Kementerian Pekerjaan Umum. Peraturan Menteri Pekerjaan Umum Nomor: 13/PRT/M/2011 Tentang Tata Cara Pemeliharaan dan Penilikan Jalan (2011). Jakarta.

Li, Z. (2005). A Probabilistic and Adaptive Approach to Modeling Performance of Pavement Infrastructure. University of Texas.

Lytton, R. L. (1987). Concepts of Pavement Performance Prediction and Modeling. In Second North American Conference on Managing Pavements (pp. 2.3-2.19). Toronto.

Ortiz-García, J. J., Costello, S. B., & Snaith, M. S. (2006). Derivation of Transition Probability Matrices for Pavement Deterioration Modeling. Journal of Transportation Engineering, 132(2), 141–161. https://doi.org/10.1061/(ASCE)0733-947X(2006)132:2(141)

Panthi, K. (2009). A Methodological Framework for Modeling Pavement Maintenance Costs for Projects with Performance-based Contracts. Florida International University. https://doi.org/10.25148/etd.FI09120824

Paterson, W. O. (1994). Highway Development and Management: A Vision of What We Need To Do A Better Job. In Proceeding of the International Workshop on HDM-4. Kuala Lumpur.

Pérez-Acebo, H., Bejan, S., & Gonzalo-Orden, H. (2017). Transition Probability Matrices for Flexible Pavement Deterioration Models with Half-Year Cycle Time. https://doi.org/10.1007/s40999-017-0254-z

Prasetyo, R. B., & Firdaus, M. (2009). Pengaruh Infrastruktur Pada Pertumbuhan Ekonomi Wilayah Di Indonesia. Jurnal Ekonomi Dan Kebijakan Pembangunan, 2, 222–236.

Surendrakumar, K., Prashant, N., & Mayuresh, P. (2013). Application Of Markovian Probabilistic Process To Develop A Decision Support System For Pavement Maintenance Management, 2(8), 295–303.

Tjan, A., & Pitaloka, D. (2005). Future Prediction of Pavement Condition Using Markov Probability Transition Matrix. In Proceedings of The Eastern Asia Society for Transportation Studies (Vol. 5, pp. 772–782).