Passenger and Revenue Estimation for New Rail Transit Lines Under Construction: A Demographic Approach

Tarisma Dwi Putri  Alifianti; Rifdatun  Ni’mah; Regita Putri  Permata

doi:10.59395/ijadis.v6i2.1420

Authors

Tarisma Dwi Putri Alifianti Telkom University
Rifdatun Ni’mah, Telkom University
Regita Putri Permata Telkom University

DOI:

https://doi.org/10.59395/ijadis.v6i2.1420

Keywords:

Rail-Based Transportation, Passenger Estimation, Revenue, Markov Chain, Fuzzy C-Means

Abstract

This study proposes a data-driven approach to estimate passenger volume and revenue for new rail transit lines under construction, addressing the challenge of limited historical data. Principal Component Analysis (PCA) was used to reduce 29 demographic variables into three principal components, which collectively captured up to 85% of the variance. These components informed a Fuzzy C-Means (FCM) clustering process that grouped new stations with existing ones based on demographic similarity. The clustering yielded a Fuzzy Partition Coefficient (FPC) of 0.913, indicating high cluster validity and low overlap between clusters. Transition probabilities of passenger flows between stations were modeled using Markov Chains. The expanded transition matrix, incorporating new stations through demographic analogy, demonstrated rapid convergence to a stationary distribution within 5–10 iterations, validating the model’s stability. Simulation results project a 57% increase in weekday passengers and a 74% increase in weekend passengers, with estimated daily revenue peaking at Rp1.216 billion. The evaluation results confirm the robustness and reliability of the combined FCM–Markov model for long-term passenger and revenue forecasting in new transit infrastructure planning.

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References

[1] I. Dostl and V. Adamec, Transport and its Role in the Society, Transactions on Transport Sciences, vol. 4, no. 2, pp. 4356, Jun. 2011, doi: 10.2478/V10158-011-0006-8. DOI: https://doi.org/10.2478/v10158-011-0006-8

[2] Badan Pusat Statistik Indonesia, Penduduk, Laju Pertumbuhan Penduduk, Distribusi Persentase Penduduk, Kepadatan Penduduk, Rasio Jenis Kelamin Penduduk Menurut Provinsi, 2024, Badan Pusat Statistik Indonesia. Accessed: Jul. 02, 2025. [Online]. Available: https://www.bps.go.id/id/statistics-table/3/V1ZSbFRUY3lTbFpEYTNsVWNGcDZjek53YkhsNFFUMDkjMw==/penduduk--laju-pertumbuhan-penduduk--distribusi-persentase-penduduk--kepadatan-penduduk--rasio-jenis-kelamin-penduduk-menurut-provinsi--2024.html?year=2024

[3] Badan Pusat Statistik Provinsi Dki Jakarta, Jumlah Kendaraan Bermotor Menurut Jenis Kendaraan (unit) di Provinsi DKI Jakarta, Badan Pusat Statistik Provinsi Dki Jakarta. Accessed: Jul. 02, 2025. [Online]. Available: https://jakarta.bps.go.id/id/statistics-table/2/Nzg2IzI=/jumlah-kendaraan-bermotor-menurut-jenis-kendaraan-unit-di-provinsi-dki-jakarta.html

[4] E. Ratnawati, MRT as an Alternative Transportation Solution which is environmentally friendly and traffic jam free in Jakarta, IOP Conf Ser Earth Environ Sci, vol. 819, no. 1, Jul. 2021, doi: 10.1088/1755-1315/819/1/012040. DOI: https://doi.org/10.1088/1755-1315/819/1/012040

[5] A. N. Hakimi Ibrahim and M. N. Borhan, The Interrelationship Between Perceived Quality, Perceived Value and User Satisfaction Towards Behavioral Intention in Public Transportation: A Review of the Evidence, Int J Adv Sci Eng Inf Technol, vol. 10, no. 5, pp. 20482056, 2020, doi: 10.18517/IJASEIT.10.5.12818. DOI: https://doi.org/10.18517/ijaseit.10.5.12818

[6] D. Bandarsyah, A. Abdulhadi, and S. Sulaeman, The Development and Economic Impact of Railway in Batavia, 1873-1930, Paramita: Historical Studies Journal, vol. 32, no. 2, pp. 159170, Sep. 2022, doi: 10.15294/PARAMITA.V32I2.31683. DOI: https://doi.org/10.15294/paramita.v32i2.31683

[7] K. M. Kasikoen, S. H. Mukti, A. Fauzi, Suprajaka, and E. Martini, Dynamic Modeling Impacts of Inter-Urban Railway Transportation on Regional Development: A Case Study of Sub-Urban Greater Jakarta, Indonesia, International Journal of Sustainable Development and Planning, vol. 18, no. 11, pp. 34193428, Nov. 2023, doi: 10.18280/IJSDP.181107. DOI: https://doi.org/10.18280/ijsdp.181107

[8] V. Padmanabhan and S. Nirmala, PREDICTABILITY OF TOTAL REVENUE IN GLOBAL LARGE COMPANIES FROM FINANCIAL AND ECONOMIC PARAMETERS.

[9] C. B. Gardner, S. D. Nielsen, M. Eltved, T. K. Rasmussen, O. A. Nielsen, and B. F. Nielsen, Calculating conditional passenger travel time distributions in mixed schedule- and frequency-based public transport networks using Markov chains, Transportation Research Part B: Methodological, vol. 152, pp. 117, Oct. 2021, doi: 10.1016/J.TRB.2021.06.020. DOI: https://doi.org/10.1016/j.trb.2021.06.020

[10] T. Spanninger, B. Bchel, and F. Corman, Train Delay Predictions Using Markov Chains Based on Process Time Deviations and Elastic State Boundaries, Mathematics 2023, Vol. 11, Page 839, vol. 11, no. 4, p. 839, Feb. 2023, doi: 10.3390/MATH11040839. DOI: https://doi.org/10.3390/math11040839

[11] M. S. Alam and S. Paul, A comparative analysis of clustering algorithms to identify the homogeneous rainfall gauge stations of Bangladesh, J Appl Stat, vol. 47, no. 8, pp. 14601481, Jun. 2020, doi: 10.1080/02664763.2019.1675606. DOI: https://doi.org/10.1080/02664763.2019.1675606

[12] Badan Pusat Statistik Provinsi Dki Jakarta, Statistik Transportasi Provinsi DKI Jakarta 2024 , 5, May 2025. Accessed: Jul. 02, 2025. [Online]. Available: https://jakarta.bps.go.id/id/publication/2025/05/23/7454766f878f35a5fa126dc5/statistik-transportasi-provinsi-dki-jakarta-2024.html

[13] R. I. Whitfield and A. H. B. Duffy, Extended revenue forecasting within a service industry, Int J Prod Econ, vol. 141, no. 2, pp. 505518, Feb. 2013, doi: 10.1016/J.IJPE.2011.11.015. DOI: https://doi.org/10.1016/j.ijpe.2011.11.015

[14] Y. Zhou, L. Wang, R. Zhong, and Y. Tan, A Markov Chain Based Demand Prediction Model for Stations in Bike Sharing Systems, 2018, doi: 10.1155/2018/8028714. DOI: https://doi.org/10.1155/2018/8028714

[15] A. P. Joshi and B. V. Patel, Data Preprocessing: The Techniques for Preparing Clean and Quality Data for Data Analytics Process, Oriental journal of computer science and technology, vol. 13, no. 0203, pp. 7881, Jan. 2021, doi: 10.13005/OJCST13.0203.03. DOI: https://doi.org/10.13005/ojcst13.0203.03

[16] J. C. Bezdek, R. Ehrlich, and W. Full, FCM: The fuzzy c-means clustering algorithm, Comput Geosci, vol. 10, no. 23, pp. 191203, 1984, doi: 10.1016/0098-3004(84)90020-7. DOI: https://doi.org/10.1016/0098-3004(84)90020-7

[17] D. Krasnov, D. Davis, K. Malott, Y. Chen, X. Shi, and A. Wong, Fuzzy C-Means Clustering: A Review of Applications in Breast Cancer Detection, Entropy 2023, Vol. 25, Page 1021, vol. 25, no. 7, p. 1021, Jul. 2023, doi: 10.3390/E25071021. DOI: https://doi.org/10.3390/e25071021

[18] O. N. Purba, D. N. Sitompul, T. H. Harahap, S. R. D. Saragih, and R. F. Siregar, Application of Fuzzy C-Means Algorithm for Clustering Customers, Hanif Journal of Information Systems, vol. 1, no. 1, pp. 2636, Aug. 2023, doi: 10.56211/hanif.v1i1.8. DOI: https://doi.org/10.56211/hanif.v1i1.8

[19] H. Abdellahoum, N. Mokhtari, A. Brahimi, and A. Boukra, CSFCM: An improved fuzzy C-Means image segmentation algorithm using a cooperative approach, Expert Syst Appl, vol. 166, p. 114063, Mar. 2021, doi: 10.1016/J.ESWA.2020.114063. DOI: https://doi.org/10.1016/j.eswa.2020.114063

[20] J. I. . Agbinya, Applied data analytics: principles and applications, p. 336, 2020, Accessed: Jul. 02, 2025. [Online]. Available: https://www.researchgate.net/publication/343810250_Markov_Chain_and_its_Applications_an_Introduction

[21] D. W. Hogg and D. Foreman-Mackey, Data Analysis Recipes: Using Markov Chain Monte Carlo*, Astrophys J Suppl Ser, vol. 236, no. 1, p. 11, May 2018, doi: 10.3847/1538-4365/AAB76E. DOI: https://doi.org/10.3847/1538-4365/aab76e

[22] L. Abd. Razid, Prediksi Curah Hujan Menggunakan Rantai Markov Untuk Perencanaan Musim Tanam Tanaman Pangan, 2019.

[23] J. Guevara, R. Hirata, and S. Canu, Cross product kernels for fuzzy set similarity, IEEE International Conference on Fuzzy Systems, Aug. 2017, doi: 10.1109/FUZZ-IEEE.2017.8015459;PAGE:STRING:ARTICLE/CHAPTER. DOI: https://doi.org/10.1109/FUZZ-IEEE.2017.8015459

[24] N. Salem and S. Hussein, Data dimensional reduction and principal components analysis, Procedia Comput Sci, vol. 163, pp. 292299, 2019, doi: 10.1016/J.PROCS.2019.12.111. DOI: https://doi.org/10.1016/j.procs.2019.12.111

[25] I. T. J. Springer, Principal Component Analysis, Second Edition.

[26] S. Messaoud, A. Bradai, S. H. R. Bukhari, P. T. A. Quang, O. Ben Ahmed, and M. Atri, A survey on machine learning in Internet of Things: Algorithms, strategies, and applications, Internet of Things, vol. 12, p. 100314, Dec. 2020, doi: 10.1016/J.IOT.2020.100314. DOI: https://doi.org/10.1016/j.iot.2020.100314

[27] J. Petrus, Ermatita, and Sukemi, Soft and Hard Clustering for Abstract Scientific Paper in Indonesian , 2019 International Conference on Informatics, Multimedia, Cyber and Information System (ICIMCIS), 2019. DOI: https://doi.org/10.1109/ICIMCIS48181.2019.8985231