Modeling Factors Influencing Passenger Decisions on Intercity and Regional Railway Train
DOI:
https://doi.org/10.30737/ukarst.v9i2.6960Keywords:
Intercity Train, Logit Model, Regional Railway Train, Personal Attributes, Travel Pattern AttributesAbstract
The dependence on private vehicles has grown significantly in the past decade, impacting travel experience quality. Transportation companies need to focus on enhancing the loyalty through exploring choice behavior. Existing research often focuses on typical service attributes, but the comparative impact with more complex variables factors remains underexplored. This study aims to identify factors influencing intercity and regional rail passenger travel decisions. This research explores sociodemographics, travel behavior, and factors affecting their mode of choice. Survey data using questionnaires were collected from 649 respondents across four intercity and five regional rail services. Logistic regression models were developed with variable selection validated using the Wald significance test and model evaluation conducted. The results indicate that intercity rail travel choices are significantly influenced by disposable income, trip purpose, and onboard comfort attributes such as air conditioning and cleanliness. In contrast, regional train choice is largely driven by factors such as occupancy rate, frequency of use, travel time reduction, and cost sensitivity. These findings confirm that intercity and regional passengers represent distinct market segments shaped by different behavioral priorities. The resulting models demonstrated strong performance, with the intercity model explaining 76.8% of the variance in choice and achieving a predictive accuracy of 82.1%, while the regional model explained 58.9% of the variance with an accuracy of 63.8%. The findings suggest that fare adjustment strategies and service development focused on air conditioning, cleanliness, and travel time can enhance ridership for both train types, thereby enhancing the overall attractiveness of the public transportation system.
References
[1] Annual Report BPS (2024). Access May 2025, https://www.bps.go.id/id/statistics-table/2/NTcjMg==/jumlah-kendaraan-bermotor.html
[2] Cerreira,R., Patricio,L., Jorge,R.N. & Magee, C. 2014. Understanding the travel experience and its impact on attitudes, emotions and loyalty towards the transportation provider–A quantitative study with mid-distance bus trips. Journal of Transport Policy Vol.31. 35-36
[3] Girsang, L.W.P. August 2023. Analysis of The Factors That Contribute To The High Rate of Private Vehicle Ownership In DKI Jakarta. Mahadi : Indonesia Journal of Law Vol.2 Issue
[4] Berry, L.L., Carbone, L.P., Haeckel, S.H., 2002. Managing the total customerexperience. MIT Sloan Manage. Rev. 43 (3), 85–89
[5] Carbone, L.P., Haeckel, H.S., 1994. Engineering customer experiences. Market. Manage. 3 (3), 8–19
[6] F Heskett, J., Reichheld, F., Sasser, W., 1990. Zero defections: quality comes to services. Harvard Bus. Rev. 68 (5),105–112.
[7] Wibowo, P.A., Ngaijan, Sumantri, A.S., Supriyanto, 2023. Analisis Faktor Pengaruh Pemilihan Kereta Api Sebagai Moda Transportasi (Studi Pada Pengguna KA. Kamandaka Rute Semarang – Purwokerto). Jurnal Ekonomi, Bisnis dan Manajemen (EBISMEN). Vol.2 No.3. 361-377.
[8] Kotler, P., & Keller, K. L. 2016. Marketing Management (15th ed.). Pearson Education.
[9] Kotler, P., & Armstrong, G. 2010.. Principles of Marketing. Prentice Hall.
[10] jiptono, Fandy. 2014, Pemasaran Jasa –Prinsip, Penerapan, dan Penelitian, Andi Offset, Yogyakarta.
[11] Van Wee B. The Unsustainability of Car Use. 2014. In Handbook of Sustainable Travel, edited by T. Gärling, D. Ettema, and M. Friman., Springer, Dordrecht, the Netherlands, pp. 69–83.
[12] Fu, Y., Sun, X. & Zhang, J. 2023. A study on the choice behavior of urban rail station connection mode considering the influence of psychological latent variables. J. Transp. Syst. Eng. Inf. Technol. https://doi.org/10.19961/j.cnki.1672-4747.2023.10.007.
[13] Hu, S., Weng, J., Zhou, W. & Lin, P. 2022. Influence of travelers’ dependence on public transportation based on extended theory of planning behavior. J. Jilin Univ. Eng. Technol. Ed. 52(05), 1037–1044. https://doi.org/10.13229/j.cnki.jdxbgxb20200985.
[14] Xiong, R. et al. 2022. The effect of the terrain slope of mountainous city on car ownership: A case study of the city of Guiyang. J. Transp. Inf. Saf. 40(05), 169–180. https://doi.org/10.3963/j.jssn.1674-4861.2022.05.018
[15] Heinitz, F. & Fritzlar, E. 2014. Reconstructing surveyed itineraries and choices between inter-city and regional train services. Pollack Period. 11(2), 111–117.
[16] Chen, B. et al. 2021. The impact of high-speed rail on intercity travel mode choice behavior: A hybrid choice model analysis. Transp. Res. Part A: Policy Pract. 153, 140–154.
[17] Zhang, Y. et al. 2021. Modelling the passenger choice behaviour of buying high-speed railway tickets. Transp. Econ. 1–20.
[18] Cao W. et al. 2020. Investigating passengers choice behavior of intercity rails with large-scale ticketing data. System Engineering Theory and Practice. 40(4), 989–1000. doi: 10.12011/1000-6788-2018-2198-12.
[19] Zhao, P. et al. 2019. Passengers choice behavior of high-speed railway considering individual heterogeneity. J. Beijing Jiaotong Univ. 43(02), 121–127. https://doi.org/10.11860/j.issn.1673-0291.2017.06.008.
[20] Sun, Y. et al. 2018. Analyzing high speed rail passengers’ train choices based on new online booking data in China. Transportation Research Part C: Emerging Technologies. 97, 96–113. doi: 10.1016/j.trc.2018.10.015.
[21] Newman, J.P., et al. 2014. Estimation of choice-based models using sales data from a single firm. Manufacturing & Service Operations Management. 16(2), 184–197. https://doi.org/10.1287/msom.2014.047.
[22] Luan, X. et al. 2020. Better understanding the choice of travel mode by urban residents: New insights from the catchment areas of rail transit stations. Sustainable Cities and Society. 53, 10196. https://doi.org/10.1016/j.scs.2019.101968.
[23] Cheng, L. et al. 2019. Applying a random forest method approach to model travel mode choice behaviour. Travel Behaviour and Society 14, 1–10. https://doi.org/10.1016/j.tbs.2018.09.002.
[24] Cheng, L. et al. 2020. Applying an ensemble-based model to travel choice behavior in travel demand forecasting under uncertainties. Transportation Letters. 12(6), 375–385. https://doi.org/10.1080/19427867.2019.1603189.
[25] Hensher,D.A., Rose,J.M., & Greene, W.H. 2005. Applied Choice Analysis: A Primer (Vol.1). Cambridge: Cambridge University Press.
[26] Farida, F. I., Sarma, M., & Indrawan, D. 2011. Analisis kepuasan pelanggan terhadap kualitas pelayanan jasa kereta api Ekspres Pakuan Jabodetabek: studi kasus kereta api Ekspres Pakuan Bogor-Jakarta. Jurnal Ilmiah Teknik Industri 10(1), 1–7.
[27] Mouwen, A. 2015. Drivers of Customers Satisfaction with Public Transport Services. Transportation Research Part A: Policy and Practice. Vol. 78, 1–20.
[28] pranata, a. o. 2014. pengukuran kepuasan pelanggan jasa layanan kereta api lokal lintas solo-yogyakarta. 3
[29] van der Waerden,P., van der Waerden,J. 2018. The Relation between Train Access Mode Attributes and Travelers’ Transport Mode-Choice Decisions in the Context of Medium- and Long-Distance Trips in the Netherlands. Transportation Research Record. Vol 2672, Issue 8. 719 – 730.
[30] Hensher, D. A., Rose, J. M., & Greene, W. H. 2005. Applied Choice Analysis: A Primer (Vol. 1). Cambridge: Cambridge University Press.
[31] Baidoo, Issac K, & Nyarko, Eric. 2015. Stated Preference Modeling for a Prefereed Transportation Mode. Ghana: iiste.org
[32] Ben-Akiva, M. and Morikawa, T. 1990. Estimation of switching models from revealed preferences and stated intentions. Transportation Research Part A: General 24(6), 485–49.
[33] Yan, H., Li, W., & Zhang, T. 2018. Research on passenger's travel mode choice behavior waiting at bus station based on SEM-Logit integration model. Sustainability. 10(6), 1996. https://doi.org/10.3390/su10061996.
[34] Chlond, B., Eisenmann, C., Heilig, M., & Vortisch, P. 2014. A hybrid modeling approach of car uses in Germany based on empirical data with different granularities. Transportation Research Record: Journal of the Transportation Research Board, 2412(1), 67–74. https://doi.org/10.3141/2412-08.
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Audinda Virsa Leinia, Sham Sidhiq
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Authors who publish with this journal agree to the following terms:
(1) The copyright of published articles will be transferred to the journal as the publisher of the manuscript. Therefore, the author needs to confirm that the copyright has been managed by the publisher with the Publication Right Form which must be attached when submitting the article.
(2) Publisher of U Karst is Kadiri University.
(3) The copyright follows Creative Commons Attribution-ShareAlike License (CC BY SA): This license allows to Share copy and redistribute the material in any medium or format, Adapt remix, transform, and build upon the material, for any purpose, even commercially.
Deprecated: json_decode(): Passing null to parameter #1 ($json) of type string is deprecated in /home/ojs.unik-kediri.ac.id/public_html/plugins/generic/citations/CitationsPlugin.php on line 68
