Model Analisis Risko Dan Ketidakpastian Prediksi Arus Lalu Lintas

Weka Indra Dharmawan

Abstract


ABSTRAK

Risiko dan ketidakpastian adalah salah satu dari berbagai permasalahan yang menjadi perhatian dalam perencanaan transportasi. Ketidakakuratan dalam memprediksi permintaan pergerakan (travel demand) merupakan gambaran suatu bentuk risiko di dalam perencanaan proyek infrastruktur transportasi. Pengalaman internasional mengisyaratkan bahwa simpangan pada prediksi tersebut sangat berpengaruh pada proyek-proyek infrastruktur jalan dan dampak risikonya, yang selama ini sering diabaikan.

 

Ada beberapa alasan yang menjadi sumber ketidakakuratan dalam memprediksi volume arus lalu lintas, diantaranya adalah terjadi force majeur (resesi ekonomi atau bencana alam) yang tidak terduga sebelumnya, sekenario tata guna lahan pada masa yang akan datang tidak pernah terealisasikan, kecendrungan sangat optimis bagi perencana yang berorientasi keuntungan (cost recovery), kesalahan dalam menetapkan besarnya tarif terhadap kesediaan membayar (willingness to pay) dari pengguna jalan tol, serta  adanya persaingan antar rute dan moda transportasi. Selain itu juga adanya keterbatasan dalam memilih model yang tepat (poor models) bisa menjadi alasan utama penyebab ketidakakurasian dalam memprediksi volume arus lalu lintas.

 

Naskah ini dimaksudkan untuk memberikan suatu gambaran terhadap model simulasi stokastik analisis ketidakpastian (uncertainty) dan risiko (risk) secara kuantitatif yang digunakan pada perencanaan transportasi menggunakan model empat tahap (four steps model) di dalam memprediksi volume lalu lintas (traffic forecasts) dari suatu investasi proyek infrastruktur jalan. Parameter statistik yang dihasilkan dari hasil model tersebut diharapkan dapat membantu pemerintah dan para investor dalam pengambilan keputusan guna menilai kelayakan investasi proyek-proyek infrastruktur transportasi khsusunya infrastruktur jalan secara ilmiah dan sistematis.

 

Kata Kunci: Analisis Ketidakpastian, Prediksi Permintaan Pergerakan, Risiko

 

ABSTRACT

 

Traffic flow prediction risk and uncertainty analysis model. Risk and uncertainty are among the many issues of concern in transportation planning. Inaccuracies in predicting travel demand represent a form of risk in planning transportation infrastructure projects. International experience suggests that deviations in these predictions significantly impact road infrastructure projects and their risk impacts, which are often overlooked.

 

There are several reasons for inaccuracies in forecasting traffic flows, including unforeseen force majeure (economic recession or natural disaster), future land use scenarios that never materialise, the tendency of profit-oriented planners to be very optimistic (cost recovery), errors in setting tariffs against the willingness of toll road users to pay, and competition between routes and modes of transport. In addition, limitations in choosing the right model (poor models) can be the main reason for inaccuracy in forecasting traffic flows.

This paper is intended to provide an overview of a stochastic simulation model for quantitative uncertainty and risk analysis used in transport planning using the four-step model in predicting traffic forecasts for road infrastructure investment projects. The statistical parameters generated from the model results are expected to help the government and investors make decisions to scientifically and systematically assess the feasibility of investing in transport infrastructure projects, especially road infrastructure.

 

Keywords: Uncertainty Analysis, Travel Demand Prediction, Traffic Risk


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References


Armoogum, J. (2003), Measuring the impact of uncertainty in travel demand modelling with a demographic approach, Paper presented at the European Transport Conference, Strasbourg, France.

Bain, R. (2002). Credit Implication of Traffic Risk in Start Up Toll Facilities. Standard & Poors, London.

Bain, R. (2003). Traffic Forecasting Risk : Study Update 2003. Standard & Poors, London.

Bain, R. (2004). Traffic Forecasting Risk : Study Update 2004. Standard & Poors, London.

Beser Hugosson, M. (2004), Quantifying uncertainties in the SAMPERS long distance forecasting system, paper presented at WCTR 2004, Istanbul.

Cools, M., B. Kochan, T. Bellemans, D. Janssens and G. Wets (2011), Assessment of the effect of microsimulation error on key travel indices: Evidence from the activity-based model Feathers, Transportation Research Record 1558, to appear.

Day, A. (2003). Mastering Risk Modeling (A Practical Guide to Modeling Uncertainty With Excel). Prentice Hall, Great Britain.

Fitriani, H., Farida, P. and Wibowo, A. (2006). Kajian Penerapan Model NPV at Risk Sebagai Alat Untuk Melakukan Evaluasi Investasi Pada Proyek Infrastruktur Jalan Tol. Jurnal Infrastruktur dan Lingkungan Binaan. Vol. II, No. 1, 1 – 12.

Kennedy, J. B. and Nevile, A. M. (1976). Basic Statistical Methods. Harper & Row Publishers, New York.

Lam, W. H. K. and Tam, M. L. (1998). Risk Analysis of Traffic and Revenue Forecasts for Road Investment Project. Journal of Infrastructure System. 19 – 27. China.

Loh, J. (2003). Rating Methodology : Toll Road Project. Rating Agency Malaysia Berhard, Kuala Lumpur.

Morlok, E. K. (1984). Pengantar Teknik dan Perencanaan Transportasi. Penerbit Erlangga, Jakarta.

Muchsin, H. (2007). Investasi Jalan Tol (Pemahaman Atas Struktur Pengusahaan, Kelayakan, Persaingan Usaha dan Kredit Investasi. Badan Penerbit Fakultas Hukum Universitas Indonesia, Jakarta.

Ortuzar, J. D. and Willumsen, L. G. (1994). Modeling Transport, Second Edition. Jhon Wiley & Sons Ltd, England.

Piyatrapoomi, N., Kumar, A. and Setunge, S. Framework for Investment Decision Making under Risk and Uncertainty for Infrastructure Asset Management. RMIT University, Melbourne, Australia.

Rodger, C and Petch, J. (1999). Uncertainty & Risk Analysis. Pricewaterhouse Coopers, United Kingdom.

Rodier, C.J. and R.A. Johnston (2002), Uncertain socioeconomic projections used in travel demand and emissions models : could plausible errors result in air quality nonconformity?, Transportation Research A, 36, 613-631.

Tamin, O. Z. (2000). Perencanaan dan Pemodelan Transportasi, Edisi II. Penerbit ITB, Bandung.

Vose, D. (1996). Quantitative Risk Analysis (A Guide to Monte Carlo Simulation Modelling). Jhon Wiley & Sons Ltd, England.

Vassallo, M. J. (2007). Traffic Risk Mitigation in Highway Concession Project. Centro De Investigación Del TransporteUniversidad Politécnica De Madrid Universidad Politecnica, Linköping (Sweden).

Wibowo, A. Manajemen Risiko Dalam Industri Jalan Tol yang Didanai Sektor Swasta di Indonesia. Departemen Teknik Sipil ITB, Bandung.

World Bank (2005). Risk & Uncertainty Analysis. Transport Note No. TRN 7, Washington, DC.

Yang, C. and A. Chen. (2011), Sensitivity-based uncertainty analysis of a combined travel demand model. Transportation Research Record, 1535.




DOI: https://doi.org/10.33024/jrets.v8i2.16581

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