Modeling public transport mode choice using stated preference: A comparative analysis of rail, bus, and shuttle services

Amelia Oktavia; Mira Wisman

doi:10.58524/jasme.v6i3.1490

Authors

Amelia Oktavia Universitas Malahayati, Indonesia
Mira Wisman Politeknik Negeri Lampung, Indonesia

DOI:

https://doi.org/10.58524/jasme.v6i3.1490

Keywords:

Intercity Transportation, Mode Choice, Multinomial Logit Model, Public Transportation, Stated Preference

Abstract

Background: Growing intercity mobility increases the need for public transport services that respond effectively to travelers’ preferences and service expectations. Understanding how passengers evaluate competing transport alternatives is therefore essential for improving mobility planning and service provision.

Aims: This study aimed to model public transport mode choice on the Kotabumi–Bandar Lampung corridor by examining passenger characteristics, identifying influential service attributes, and estimating choice probabilities for rail, bus, and shuttle services.

Method: A quantitative stated preference approach was employed with 384 respondents, comprising 234 offline and 150 online participants. Data were analyzed using validity and reliability tests, multiple linear regression, utility estimation, multinomial logit modeling, and Pearson correlation.

Results: Comfort was the leading consideration for rail users (27%), while fare was most prominent for bus (33%) and shuttle users (26%). The combined choice probabilities were 48.65% for rail, 25.86% for bus, and 25.49% for shuttle, indicating a clear preference for rail.

Conclusion: Rail emerged as the most preferred public transport alternative, while differences in mode-specific considerations highlight the importance of incorporating passenger preferences into intercity transport planning and service improvement.

References

Agaton, C. B., Collera, A. A., & Guno, C. S. (2020). Socio-economic and environmental analyses of sustainable public transport in the Philippines. Sustainability, 12(11), 4720. https://doi.org/10.3390/su12114720

Benmessaoud, Y., Cherrat, L., & Ezziyyani, M. (2023). Real-time self-adaptive traffic management system for optimal vehicular navigation in modern cities. Computers, 12(4), 80. https://doi.org/10.3390/computers12040080

Bertsimas, D., Ng, Y. S., & Yan, J. (2020). Joint frequency-setting and pricing optimization on multimodal transit networks at scale. Transportation Science, 54(3), 839–853. https://doi.org/10.1287/trsc.2019.0959

Bickel, J., Geržinič, N., van Oort, N., de Bruyn, M., & Molin, E. (2026). Heterogeneity in route choice behaviour during unplanned train disruptions considering the possibility of teleworking. Transportation Research Part A: Policy and Practice, 203, 104708. https://doi.org/10.1016/j.tra.2025.104708

Bösehans, G., & Walker, I. (2020). Do supra-modal traveller types exist? A travel behaviour market segmentation using goal framing theory. Transportation, 47(1), 243–273. https://doi.org/10.1007/s11116-018-9874-7

Ceder, A. (2021). Urban mobility and public transport: Future perspectives and review. International Journal of Urban Sciences, 25(4), 455–479. https://doi.org/10.1080/12265934.2020.1799846

Cornet, Y., Lugano, G., Georgouli, C., & Milakis, D. (2022). Worthwhile travel time: A conceptual framework of the perceived value of enjoyment, productivity and fitness while travelling. Transport Reviews, 42(5), 580–603. https://doi.org/10.1080/01441647.2021.1983067

Dällenbach, N. (2020). Low-carbon travel mode choices: The role of time perceptions and familiarity. Transportation Research Part D: Transport and Environment, 86, 102378. https://doi.org/10.1016/j.trd.2020.102378

Das, S., Boruah, A., Banerjee, A., Raoniar, R., Nama, S., & Maurya, A. K. (2021). Impact of COVID-19: A radical modal shift from public to private transport mode. Transport Policy, 109, 1–11. https://doi.org/10.1016/j.tranpol.2021.05.005

de Almeida Costa, M., Kaddoura, I., Noll, A., Unterfinger, M., Balać, M., & Corman, F. (2025). Exploring the role of an underground logistics system in enhancing Swiss intermodal freight. Transportation Engineering, 21, 100375. https://doi.org/10.1016/j.treng.2025.100375

De Vos, J., Singleton, P. A., & Gärling, T. (2022). From attitude to satisfaction: Introducing the travel mode choice cycle. Transport Reviews, 42(2), 204–221. https://doi.org/10.1080/01441647.2021.1958952

Dixit, M., & Sivakumar, A. (2020). Capturing the impact of individual characteristics on transport accessibility and equity analysis. Transportation Research Part D: Transport and Environment, 87, 102473. https://doi.org/10.1016/j.trd.2020.102473

Eldeeb, G., & Mohamed, M. (2020). Quantifying preference heterogeneity in transit service desired quality using a latent class choice model. Transportation Research Part A: Policy and Practice, 139, 119–133. https://doi.org/10.1016/j.tra.2020.07.006

Esztergár-Kiss, D., Shulha, Y., Aba, A., & Tettamanti, T. (2021). Promoting sustainable mode choice for commuting supported by persuasive strategies. Sustainable Cities and Society, 74, 103264. https://doi.org/10.1016/j.scs.2021.103264

Fale, M., Wang, Y., Rupnik, B., Kramberger, T., & Vizinger, T. (2025). Systematic review of transportation choice modeling. Applied Sciences, 15(17), 9235. https://doi.org/10.3390/app15179235

Fazi, S., & Fransoo, J. C. (2025). Freight mobility as a service: Open platforms for synchromodal transport. Transportation Research Part E: Logistics and Transportation Review, 204, 104368. https://doi.org/10.1016/j.tre.2025.104368

Feng, Y., Zhao, J., Sun, H., Wu, J., & Gao, Z. (2022). Choices of intercity multimodal passenger travel modes. Physica A: Statistical Mechanics and Its Applications, 600, 127500. https://doi.org/10.1016/j.physa.2022.127500

Fitschen, P., Merfeld, K., Klein, J. F., & Henkel, S. (2024). Understanding the urban mobility challenge: Why shared mobility providers fail to attract car drivers. Transport Policy, 158, 104–111. https://doi.org/10.1016/j.tranpol.2024.09.010

Friman, M., Lättman, K., & Olsson, L. E. (2020). Public transport quality, safety, and perceived accessibility. Sustainability, 12(9), 3563. https://doi.org/10.3390/su12093563

Gadepalli, R., Tiwari, G., & Bolia, N. (2020). Role of users’ socio-economic and travel characteristics in mode choice between city bus and informal transit services: Lessons from household surveys in Visakhapatnam, India. Journal of Transport Geography, 88, 102307. https://doi.org/10.1016/j.jtrangeo.2018.08.017

Ge, L., Voß, S., & Xie, L. (2022). Robustness and disturbances in public transport. Public Transport, 14(1), 191–261. https://doi.org/10.1007/s12469-022-00301-8

Gholi, H., Kermanshah, M., & Mamdoohi, A. R. (2022). Investigating the sources of heterogeneity in passengers’ preferences for transit service quality. Journal of Public Transportation, 24, 100014. https://doi.org/10.1016/j.jpubtr.2022.100014

Göransson, J., & Andersson, H. (2023). Factors that make public transport systems attractive: A review of travel preferences and travel mode choices. European Transport Research Review, 15(1), 32. https://doi.org/10.1186/s12544-023-00609-x

Guo, J., & Yang, M. (2026). Perceptions of inter-city commuters toward high-speed rail and transfer services quality: An importance-performance analysis of the Suzhou-Shanghai corridor. Transport Policy, 179, 103985. https://doi.org/10.1016/j.tranpol.2025.103985

Han, B., Ren, S., & Bao, J. (2020). Mixed logit model based on improved nonlinear utility functions: A market shares solution method of different railway traffic modes. Sustainability, 12(4), 1406. https://doi.org/10.3390/su12041406

Hidayati, I., Tan, W., & Yamu, C. (2021). Conceptualizing mobility inequality: Mobility and accessibility for the marginalized. Journal of Planning Literature, 36(4), 492–507. https://doi.org/10.1177/08854122211012898

Huan, N., Hess, S., Yamamoto, T., & Yao, E. (2026). Modelling intermodal traveller behaviour in mega-city regions: Simultaneous versus sequential estimation frameworks. Transportation, 53(1), 35–70. https://doi.org/10.1007/s11116-024-10489-2

Hunecke, M., Groth, S., & Wittowsky, D. (2020). Young social milieus and multimodality: Interrelations of travel behaviours and psychographic characteristics. Mobilities, 15(3), 397–415. https://doi.org/10.1080/17450101.2020.1732099

Jang, S., Caiati, V., Rasouli, S., Timmermans, H., & Choi, K. (2021). Does MaaS contribute to sustainable transportation? A mode choice perspective. International Journal of Sustainable Transportation, 15(5), 351–363. https://doi.org/10.1080/15568318.2020.1783726

Kožul, N., Novačko, L., Babojelić, K., & Brlek, P. (2025). Stated preference approach in shaping urban sustainable multimodal transport: A literature review. Systems, 13(10), 853. https://doi.org/10.3390/systems13100853

Kyi, E. P., Feng, T., Lan, J., & Liu, Y. (2026). Destination drone: A comprehensive analysis of Japanese consumer choice behavior and intentions for drone delivery services. Research in Transportation Business & Management, 67, 101673. https://doi.org/10.1016/j.rtbm.2026.101673

Leffler, D., Burghout, W., Cats, O., & Jenelius, E. (2024). An adaptive route choice model for integrated fixed and flexible transit systems. Transportmetrica B: Transport Dynamics, 12(1), 2303047. https://doi.org/10.1080/21680566.2024.2303047

Li, X., Ma, R., Guo, Y., Wang, W., Yan, B., & Chen, J. (2021). Investigation of factors and their dynamic effects on intercity travel modes competition. Travel Behaviour and Society, 23, 166–176. https://doi.org/10.1016/j.tbs.2021.01.003

Li, X., Shi, L., Shi, Y., Tang, J., Zhao, P., Wang, Y., & Chen, J. (2024). Exploring interactive and nonlinear effects of key factors on intercity travel mode choice using XGBoost. Applied Geography, 166, 103264. https://doi.org/10.1016/j.apgeog.2024.103264

Liu, Y.-A., Jing, Y., & Guo, S.-Y. (2025). Service attribute preferences of Air-HSR transfer process from a combined perspective of resource-matching, perceived risk, and value theories. Journal of Advanced Transportation, 2025(1), 7498989. https://doi.org/10.1155/atr/7498989

Mahani, S. F., Oliveira, B. B., Patrício, L., Miguéis, V., Carravilla, M. A., & Oliveira, J. F. (2026). Behavior and factors of choice of urban travelers: A data-driven approach to sustainable mobility. Transportation. Advance online publication. https://doi.org/10.1007/s11116-026-10765-3

Malichová, E., Cornet, Y., & Hudák, M. (2022). Travellers’ use and perception of travel time in long-distance trips in Europe. Travel Behaviour and Society, 27, 95–106. https://doi.org/10.1016/j.tbs.2021.12.003

Merkert, R., Bushell, J., & Beck, M. J. (2020). Collaboration as a service (CaaS) to fully integrate public transportation: Lessons from long-distance travel to reimagine mobility as a service. Transportation Research Part A: Policy and Practice, 131, 267–282. https://doi.org/10.1016/j.tra.2019.09.025

Minhans, A., Chatterjee, A., & Popli, S. (2020). Public perceptions: An important determinant of transport users’ travel behaviour. Human Geographies – Journal of Studies and Research in Human Geography, 14(2). https://doi.org/10.5719/hgeo.2020.142.1

Mohammadi, R., Vosough, S., & Roncoli, C. (2024). Analysing the environmental and social impacts of a novel user-based transit signal priority strategy in a connected vehicle environment. Journal of Advanced Transportation, 2024(1), 8712813. https://doi.org/10.1155/2024/8712813

Narayan, J., Cats, O., van Oort, N., & Hoogendoorn, S. (2020). Integrated route choice and assignment model for fixed and flexible public transport systems. Transportation Research Part C: Emerging Technologies, 115, 102631. https://doi.org/10.1016/j.trc.2020.102631

Owais, M., Salah, A., & Shehata, H. M. (2026). Integrating machine learning and global sensitivity analysis for modeling public transport acceptance: Evidence from Egyptian cities. Transportation Planning and Technology. Advance online publication. https://doi.org/10.1080/03081060.2026.2642868

Park, J., Choi, S., Kim, G., & Jo, H. (2025). Provision of metropolitan demand responsive transit and attitude’s role in mode choice. Transportation Research Part D: Transport and Environment, 140, 104640. https://doi.org/10.1016/j.trd.2025.104640

Peng, W., Teng, J., & Wang, H. (2024). Understanding heterogeneous passenger route choice in municipal rail transit with express and local trains: An empirical study in Shanghai. Urban Rail Transit, 10(2), 122–143. https://doi.org/10.1007/s40864-024-00214-8

Pereira, R. H. M., Herszenhut, D., Saraiva, M., & Farber, S. (2024). Ride-hailing and transit accessibility considering the trade-off between time and money. Cities, 144, 104663. https://doi.org/10.1016/j.cities.2023.104663

Pourhashem, G., Georgouli, C., Malichová, E., Straka, M., & Kováčiková, T. (2024). Factors influencing the perceived value of travel time in European urban areas. Transportation, 51(4), 1525–1545. https://doi.org/10.1007/s11116-023-10376-2

Pourhashem, G., Malichová, E., Piscová, T., & Kováčiková, T. (2022). Gender difference in perception of value of travel time and travel mode choice behavior in eight European countries. Sustainability, 14(16), 10426. https://doi.org/10.3390/su141610426

Putra, F. A., Wibowo, A., & Sidiq, I. P. A. (2022). Local climate zone (LCZ) in Bandar Lampung City. International Journal of Remote Sensing and Earth Sciences, 19(1), 69–78. https://doi.org/10.30536/ijreses.v19i1.13791

Rahma, S., Triono, A., & A.T., M. E. P. (2024). Implementing social security programs for employees in Bandar Lampung: Challenges and recommendations. Journal of Governance and Accountability Studies, 3(2), 109–119. https://doi.org/10.35912/jgas.v3i2.1873

Ram, Y., Gal-Tzur, A., & Rechavi, A. (2021). Identifying attributes of public transport services for urban tourists: A data-mining method. Journal of Transport Geography, 93, 103069. https://doi.org/10.1016/j.jtrangeo.2021.103069

Ranjan, R., & Sinha, S. (2026). Influence of integrated multimodal travel information on commuter preferences and mode choice in urban areas. Journal of Engineering and Applied Science, 73(1), 59. https://doi.org/10.1186/s44147-026-00918-7

Rasheed, M. R. (2023). Moving forward: An assessment of Qatar’s public transportation system and recommendations for future development [Master’s thesis, Politecnico di Milano]. POLITesi. https://hdl.handle.net/10589/211512

Silver, K., Lopes, A., Vale, D., & da Costa, N. M. (2023). The inequality effects of public transport fare: The case of Lisbon’s fare reform. Journal of Transport Geography, 112, 103685. https://doi.org/10.1016/j.jtrangeo.2023.103685

Sukhov, A., Olsson, L. E., & Friman, M. (2022). Necessary and sufficient conditions for attractive public transport: Combined use of PLS-SEM and NCA. Transportation Research Part A: Policy and Practice, 158, 239–250. https://doi.org/10.1016/j.tra.2022.03.012

Thombre, A., & Agarwal, A. (2021). A paradigm shift in urban mobility: Policy insights from travel before and after COVID-19 to seize the opportunity. Transport Policy, 110, 335–353. https://doi.org/10.1016/j.tranpol.2021.06.010

Wang, K., Bhat, C. R., & Ye, X. (2023). A multinomial probit analysis of Shanghai commute mode choice. Transportation, 50(4), 1471–1495. https://doi.org/10.1007/s11116-022-10284-x

Wong, Y. Z., Hensher, D. A., & Mulley, C. (2020). Mobility as a service (MaaS): Charting a future context. Transportation Research Part A: Policy and Practice, 131, 5–19. https://doi.org/10.1016/j.tra.2019.09.030

Xu, G., Zhang, R., Xu, S. X., Kou, X., & Qiu, X. (2021). Personalized multimodal travel service design for sustainable intercity transport. Journal of Cleaner Production, 308, 127367. https://doi.org/10.1016/j.jclepro.2021.127367

Yousefzadeh Barri, E., Farber, S., Jahanshahi, H., Tiznado-Aitken, I., & Beyazit, E. (2025). Exploring the joint impacts of income, car ownership, and built environment on daily activity patterns: A cluster analysis of trip chains. Transportmetrica A: Transport Science, 21(1), 2236235. https://doi.org/10.1080/23249935.2023.2236235

Yu, W., Wen, H., Wang, W., Zhao, D., & Hua, X. (2024). On the calibration and improvement of human mobility models in intercity transportation system. Physica A: Statistical Mechanics and Its Applications, 653, 130116. https://doi.org/10.1016/j.physa.2024.130116

Zhao, X., Yan, X., Yu, A., & Van Hentenryck, P. (2020). Prediction and behavioral analysis of travel mode choice: A comparison of machine learning and logit models. Travel Behaviour and Society, 20, 22–35. https://doi.org/10.1016/j.tbs.2020.02.003

Zhou, H., Dorsman, J. L., Mandjes, M., & Snelder, M. (2025). A tour-based multimodal mode choice model for impact assessment of new mobility concepts and mobility as a service. Transportation, 52(3), 895–921. https://doi.org/10.1007/s11116-023-10443-8