Modeling the Optimal Toll Tariff Value Using a Dynamic System (Case Study of the Tebingtinggi-Sinaksak Toll Gate) 3

Muliandi Muliandi; M. Ridwan Anas; Gina Cynthia Raphita  Hasibuan

doi:10.70716/reswara.v4i4.932

Authors

Muliandi Master of Civil Engineering Study Program, Faculty of Engineering, University of North Sumatra, Indonesia Author
M. Ridwan Anas Master of Civil Engineering Study Program, Faculty of Engineering, University of North Sumatra, Indonesia Author
Gina Cynthia Raphita Hasibuan Master of Civil Engineering Study Program, Faculty of Engineering, University of North Sumatra, Indonesia Author

DOI:

https://doi.org/10.70716/reswara.v4i4.932

Keywords:

optimum toll rates, dynamic system, BKBOK, toll revenue, net present value

Abstract

Toll road tariff determination needs to balance user benefits, business entity revenues, and investment feasibility. This study develops an optimum toll tariff model at the Tebing Tinggi–Sinaksak Toll Gate using a dynamic system approach and Vensim software. Input data include road and traffic conditions, willingness to pay (WTP), Vehicle Operating Cost Profit (BKBOK), investment costs, and operation and maintenance costs. Simulations were conducted for 50 years with a two-year time step in scenarios of 30%, 50%, 70%, and 100% BKBOK. The results show that tariff increases reduce the number of users switching to toll roads, so that the highest tariff does not always generate the highest revenue. The 30% scenario results in a tariff of Rp751.42/km, 7,654 vehicles, and revenue of Rp1.49069 trillion/year; the 50% scenario results in a tariff of Rp974.74/km, 6,958 vehicles, and the highest revenue of Rp1.53884 trillion/year; The 70% scenario yields a tariff of Rp1,231.12/km and revenue of Rp1.47872 trillion/year; while the 100% scenario yields the lowest revenue of Rp1.01643 trillion/year. The NPV analysis shows that the 50% BKBOK scenario has the highest value at discount rates of 6%, 8%, and 10%, and remains feasible up to a 10% discount. Thus, the tariff of Rp974.74/km is the best alternative based on the balance of tariff, users, revenue, and investment feasibility.

References

Agénor, P. R. (2010). A theory of infrastructure-led development. Journal of Economic Dynamics and Control, 34(5), 932–950. https://doi.org/10.1016/j.jedc.2010.01.009

Albalate, D., Bel, G., & Fageda, X. (2007). Privatization and regulation of toll motorways in Europe.

Alfani, A. M., Donoriyanto, D. S., & Nugraha, I. (2023). Penerapan Sistem Dinamis dalam Menganalisis Tarif Tol Krian-Legundi-Manyar-Bunder. Briliant: Jurnal Riset dan Konseptual, 8(3), 704-714. https://doi.org/10.28926/briliant.v8i3.1192

Anas, M. R., Hasibuan, G. C. R., & Nasution, M. I. (2024). Determining initial toll road tariff based on BKBOK, ATP and WTP: Case study Tebing Tinggi–Pematangsiantar toll road section. International Journal of Architecture and Urbanism, 8(3), 401–407. https://doi.org/10.32734/ijau.v8i3.16953

Anggoro, Y. T., & Anas, E. P. (2024). Analisa kemungkinan penerapan tarif tol dinamis: Studi kasus pada jalan tol dalam Kota Jakarta. https://doi.org/10.36418/syntax

Artika, I., & Chaerul, M. (2020). Model sistem dinamik untuk evaluasi skenario pengelolaan sampah di Kota Depok. Jurnal Wilayah Dan Lingkungan, 8(3), 261–279. https://doi.org/10.14710/jwl.8.3.261-279

Borndörfer, R., Karbstein, M., & Pfetsch, M. E. (2012). Models for fare planning in public transport. Discrete Applied Mathematics, 160(18), 2591–2605. https://doi.org/10.1016/j.dam.2012.02.027

Chaerul, M., Tanaka, M., & Shekdar, A. V. (2008). A system dynamics approach for hospital waste management. Waste Management, 28(2), 442–449. https://doi.org/10.1016/j.wasman.2007.01.007

Federal Highway Administration. (2010). Reducing congestion and funding transportation using road pricing in Europe and Singapore. https://doi.org/https://doi.org/10.1016/j.wasman.2007.01.007

Hermawan, R., Frazila, R. B., Awang, A., & Jihanny, J. (2013). Hubungan antara variasi tarif tol dengan pendapatan dan tingkat pelayanan. Jurnal Teknik Sipil, 20(1), 55–66. https://doi.org/10.5614/jts.2013.20.1.7

Indonesia, P. R. (2005). Peraturan Pemerintah Nomor 15 Tahun 2005 tentang Jalan Tol. https://doi.org/https://jdih.kemenkeu.go.id/dok/pp-15-tahun-2005

Jia, Y. H., & Tian, Z. Z. (2002). Toll rates and related factors on China’s highways. Transportation Research Record, 1812, 22–26. https://doi.org/10.3141/1812-03

Panou, K. (2020). An empirical model for setting and adjusting road-toll rates based on optimized equity and affordability strategies. Case Studies on Transport Policy, 8(4), 1256–1269. https://doi.org/10.1016/j.cstp.2020.08.006

Shelton, J., & Martin, P. (2019). Determining optimal tolling rates for border regions using dynamic modeling methods. American Journal of Traffic and Transportation Engineering, 4(4), 118. https://doi.org/10.11648/j.ajtte.20190404.12

Shirazi, M., Aashtiani, H. Z., & Quadrifoglio, L. (2017). Estimating the minimal revenue tolls in large-scale roadway networks using the dynamic penalty function method. Computers & Industrial Engineering, 107, 120–127. https://doi.org/10.1016/j.cie.2017.03.008

Sterman, J. D. (2000). Business dynamics: Systems thinking and modeling for a complex world. McGraw-Hill. https://doi.org/Business Dynamics - MIT Sloan Center for Process Innovation

Tamin, O. Z. (2000). Perencanaan dan pemodelan transportasi (Edisi kedua). Institut Teknologi Bandung.

Tol, B. P. J. (2024). Jalan tol yang berkeselamatan: Laporan tahunan BPJT 2024. https://doi.org/https://bpjt.pu.go.id/wp-content/uploads/FINAL-LAPORAN-BADAN-PENGATUR-JALAN-TOL-2024_compressed.pdf

Wibowo, A., & Santoso, S. R. (2022). Model parametrik batas atas dan bawah tarif tol di Indonesia menggunakan stochastic frontier analysis. Jurnal Teknik Sipil, 29(1), 89–98. https://doi.org/10.5614/jts.2022.29.1.9

Zahedian, S., Nohekhan, A., & Sadabadi, K. F. (2021). Dynamic toll prediction using historical data on toll roads: Case study of the I-66 Inner Beltway. Transportation Engineering, 5, 100084. https://doi.org/10.1016/j.treng.2021.100084