Modeling Tourist Transportation Mode Choice and Trip Chains Through Key Influencing Factors

Indriaty Wulansari; Sumarni Hamid Aly; M. Isran Ramli; Muralia Hustim

doi:10.28991/CEJ-2025-011-02-017

Authors

Indriaty Wulansari
indriaty.wulansari@gmail.com
Department of Civil Engineering, Faculty of Engineering, Universitas Hasanuddin, Gowa, 92171,, Indonesia https://orcid.org/0000-0002-6768-1934
Sumarni Hamid Aly Department of Civil Engineering, Faculty of Engineering, Universitas Hasanuddin, Gowa, 92171,, Indonesia
M. Isran Ramli Department of Civil Engineering, Faculty of Engineering, Universitas Hasanuddin, Gowa, 92171,, Indonesia
Muralia Hustim Department of Civil Engineering, Faculty of Engineering, Universitas Hasanuddin, Gowa, 92171,, Indonesia

Vol. 11 No. 2 (2025): February

Research Articles

Downloads

PDF

Abstract
How to Cite
Metrics
References
License

To understand tourist behavior and the factors influencing it, a thorough analysis of transportation mode choice and trip chain is required, especially from the tourists' perspective. Therefore, this study aims to model transportation mode choice and trip chain in the Bira Peninsula, Bulukumba Regency, South Sulawesi. To achieve this, a quantitative method was employed with a sample size of 500 tourists. The study results show that independent variables significantly impact dependent variables, such as individual characteristics, movement characteristics, destination attributes, mode choice attributes, and trip chains. Several indicators showed a significant influence for transportation mode choice with a confidence level of over 85%. These indicators include age, income, origin location, destination location, number of visits, group size, flexibility, facilities, ease of access, activity type, cost, distance, time, and safety. Similarly, the analysis identified several key indicators affecting the trip chain, with a significance level above 85%. These indicators include age, income, origin, destination location, estimated arrival time, number of visits, flexibility, and destination attraction. Other indicators include facilities, ease of access, trip purpose, activity type, travel time, distance from the city center to the tourist destination, cost, distance, time, and safety. Two significant indicators found as differentiators from previous research are flexibility and type of activity. The study demonstrated high accuracy for the mode choice model and the trip chain model, with validity rates of 98.40% and 97.65%, respectively. The findings indicate that the model accurately captures the factors influencing transportation mode choice and trip chains, making it a valuable reference for future explorations to improve transportation systems' efficiency and comfort.

Doi: 10.28991/CEJ-2025-011-02-017

Full Text: PDF

Wulansari, I., Hamid Aly, S., Isran Ramli, M., & Hustim, M. (2025). Modeling Tourist Transportation Mode Choice and Trip Chains Through Key Influencing Factors. Civil Engineering Journal, 11(2), 688–703. https://doi.org/10.28991/CEJ-2025-011-02-017

Download Citation

[1] Fadilla, H. (2024). Development of the Tourism Sector to Increase Regional Income in Indonesia. Benefit: Journal of Business, Economics, and Finance, 22(1), 36-43. doi:10.37985/benefit.v2i1.375.
[2] Singh, D., & Alam, Q. (2024). Is tourism expansion the key to economic growth in India? An aggregate-level time series analysis. Annals of Tourism Research Empirical Insights, 5(2), 100126. doi:10.1016/j.annale.2024.100126.
[3] Patandianan, M. V., & Shibusawa, H. (2020). Importance and performance of streetscapes at a tourism destination in Indonesia: The residents' perspectives. Frontiers of Architectural Research, 9(3), 641–655. doi:10.1016/j.foar.2020.05.006.
[4] Bakri, S., Romlah, D. R., Setiawan, A., & Aryanti, N. Y. (2024). Model of revisit intention as the basis for tourism object development planning in South Lampung. AIP Conference Proceedings, 3001(1), 080032. doi:10.1063/5.0184131.
[5] Yatmar, H., Ramli, M. I., Adisasmita, S. A., & Chaerunnisa, A. S. (2024). Exploring Trip Chain Pattern Choices at Urban Relaxation Facilities: A Case Study of Lego-Lego in Central Point of Indonesia. International Journal on Advanced Science, Engineering and Information Technology, 14(2), 582–591. doi:10.18517/ijaseit.14.2.19324.
[6] Chen, S., Sotiriadis, M., & Shen, S. (2023). The influencing factors on service experiences in rural tourism: An integrated approach. Tourism Management Perspectives, 47. doi:10.1016/j.tmp.2023.101122.
[7] Wulansari, I., Aly, S. H., Ramli, M. I., & Hustim, M. (2023). Tourist Prospects and Availability of Accommodation Infrastructure in the Bira Peninsula Tourism Area, Bulukumba Regency. Proceeding Civil Engineering, Environmental, Disaster, and Risk Management Symposium, Yogyakarta, Indonesia.
[8] Wulansari, I., Aly, S. H., Ramli, M. I., & Hustim, M. (2023). Tangible External Factors of Destination Selection in the Bira Peninsula Tourism Area, Bulukumba Regency. Proceeding: Inter-university Transportation Studies Forum (FSTPT), 11(1), 1–9.
[9] Aisah, N., & Suseno, D. A. (2021). Analysis of Transportation Mode Selection in Tourist Visits. Efficient: Indonesian Journal of Development Economics, 4(1), 1108–1127. doi:10.15294/efficient.v4i1.43274. (Indonesian).
[10] Hermawati, P., Adisasmita, S., Ramli, M. I., & Hamid, S. (2019). Choices models of trip chain and transportation mode for international tourists in tourism destination Island. GEOMATE Journal, 16(55), 195-203.).
[11] Marrocu, E., & Paci, R. (2013). Different tourists to different destinations. Evidence from spatial interaction models. Tourism Management, 39, 71-83. doi:10.1016/j.tourman.2012.10.009.
[12] Subbarao, S. S. V., Swaroop, S. N. V., & Shekhar, R. S. (2020). Interrelationships between Mode Choice and Trip-chain Choice decisions: A case of Mumbai Metropolitan Region. Transportation Research Procedia, 48, 3049–3061. doi:10.1016/j.trpro.2020.08.182.
[13] Qi, C., Zhu, Z., Guo, X., Lu, R., & Chen, J. (2020). Examining interrelationships between tourist travel mode and trip chain choices using the nested logit model. Sustainability (Switzerland), 12(18), 7535. doi:10.3390/su12187535.
[14] Bayih, B. E., & Singh, A. (2020). Modeling domestic tourism: motivations, satisfaction and tourist behavioral intentions. Heliyon, 6(9), 4839. doi:10.1016/j.heliyon.2020.e04839.
[15] Lei, J., & Suntrayuth, S. (2023). A Study on the Impact of Chinese Domestic Tourists' Participation on Their Post-Travel Behaviors in Traditional Chinese Villages. Heritage, 6(7), 5187–5201. doi:10.3390/heritage6070275.
[16] Li, X., Hossein Rashidi, T., & Koo, T. T. R. (2023). Tourists' travel mode and length of stay: Application of a fully nested Archimedean copula structure. Transportation Research Part A: Policy and Practice, 172, 103678. doi:10.1016/j.tra.2023.103678.
[17] Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019). When to use and how to report the results of PLS-SEM. European Business Review, 31(1), 2–24. doi:10.1108/EBR-11-2018-0203.
[18] Sarstedt, M., Ringle, C.M., & Hair, J.F. (2022). Partial Least Squares Structural Equation Modeling. Handbook of Market Research. Springer, Cham, Switzerland. doi:10.1007/978-3-319-57413-4_15.
[19] Sarstedt, M., Hair, J. F., Cheah, J. H., Becker, J. M., & Ringle, C. M. (2019). How to specify, estimate, and validate higher-order constructs in PLS-SEM. Australasian Marketing Journal, 27(3), 197–211. doi:10.1016/j.ausmj.2019.05.003.
[20] Harz, J., & Sommer, C. (2022). Mode choice of city tourists: Discrete choice modeling based on survey data from a major German city. Transportation Research Interdisciplinary Perspectives, 16, 100704. doi:10.1016/j.trip.2022.100704.
[21] Yorika, R., Nugroho, R. A., & Syafitri, E. D. (2021). Analysis of Visitor Characteristics of Balikpapan Botanical Gardens Tourist Attraction. JSHP: Jurnal Sosial Humaniora Dan Pendidikan, 5(2), 1079. doi:10.32487/jshp.v5i2.1079. (Indonesian).
[22] Zientara, P., JaŠ¼dŠ¼ewska-Gutta, M., BÄ…k, M., & Zamojska, A. (2024). What drives tourists' sustainable mobility at city destinations? Insights from ten European capital cities. Journal of Destination Marketing & Management, 33, 100931. doi:10.1016/j.jdmm.2024.100931.
[23] Aktamati, W., & Fatharani, A. (2024). The Influence of Tourism Attraction and Tourist Perception on Visiting Decision at Mount Telomoyo. Wahana Pendidikan Scientific Journal, 10(7), 1087-1096. doi:10.5281/zenodo.11351808.
[24] Thrane, C. (2015). Examining tourists' long-distance transportation mode choices using a Multinomial Logit regression model. Tourism Management Perspectives, 15, 115–121. doi:10.1016/j.tmp.2014.10.004.
[25] Romí£o, J., & Bi, Y. (2021). Determinants of collective transport mode choice and its impacts on trip satisfaction in urban tourism. Journal of Transport Geography, 94, 103094. doi:10.1016/j.jtrangeo.2021.103094.
[26] Esztergár-Kiss, D. (2020). Trip chaining model with classification and optimization parameters. Sustainability (Switzerland), 12(16), 6422. doi:10.3390/SU12166422.
[27] Waruwu, M. (2024). Metode Penelitian dan Pengembangan (R & D): Concept, Types, Stages and Advantages. Jurnal Ilmiah Profesi Pendidikan, 9(2), 1220–1230. doi:10.29303/jipp.v9i2.2141. (Indonesian).
[28] El-Habil, A. M. (2012). An application on multinomial logistic regression model. Pakistan Journal of Statistics and Operation Research, 8(2), 271–291. doi:10.18187/pjsor.v8i2.234.
[29] Gu, Y., Hole, A. R., & Knox, S. (2013). Fitting the generalized multinomial logit model in Stata. The Stata Journal, 13(2), 382–397. doi:10.1177/1536867x1301300213.
[30] Lamondia, J., & Fisher, M. (2024). Can we focus long-distance travel survey sampling? Analyzing travel behavior trends between and within demographic groups. Transportation Research Procedia, 76, 520–533. doi:10.1016/j.trpro.2023.12.073.
[31] Kock, N. (2018). Minimum Sample Size Estimation in PLS-SEM: An Application in Tourism and Hospitality Research. Applying Partial Least Squares in Tourism and Hospitality Research, Emerald Publishing Limited, Bingley, United Kingdom. doi:10.1108/978-1-78756-699-620181001.
[32] Chin, W. W. (1998). The partial least squares approach to structural equation modeling. Modern methods for business research, Lawrence Erlbaum Associates, Mahwah, United States.
[33] Maltese, I., Crotti, D., Marcucci, E., Gatta, V., & Scaccia, L. (2025). Multimodality at destination: A focus on domestic tourism. Research in Transportation Business & Management, 58, 101249. doi:10.1016/j.rtbm.2024.101249.
[34] Domínguez Vila, T., & Darcy, S. (2025). Beyond technical website compliance: Identifying and assessing accessible tourism value chain information content on national tourism organisation websites. Tourism Management Perspectives, 55, 101332. doi:10.1016/j.tmp.2024.101332.
[35] 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. doi:10.1016/j.tbs.2020.02.003.
[36] Blättler, K., Wallimann, H., & von Arx, W. (2024). Free public transport to the destination: A causal analysis of tourists' travel mode choice. Transportation Research Part A: Policy and Practice, 187, 104166. doi:10.1016/j.tra.2024.104166.
[37] Zhang, B., Zhang, J., & Xu, Q. (2024). Design of rural tourism management cloud platform based on intelligent audio processing and sensor navigation positioning. Measurement: Sensors, 33, 101101. doi:10.1016/j.measen.2024.101101.
[38] Domènech, A., Miravet, D., & Gutiérrez, A. (2023). Tourists' transport modal choices in Barcelona. Research in Transportation Business & Management, 48. doi:10.1016/j.rtbm.2022.100902.
[39] Cartení¬, A., Henke, I., & Picone, M. (2024). The value of waiting spaces: Tourists' willingness to pay for high-quality bus terminals. Transportation Research Interdisciplinary Perspectives, 26, 101129. doi:10.1016/j.trip.2024.101129.
[40] Rodrigues, V., Breda, Z., & Rodrigues, C. (2024). The implications of industry 4.0 for the tourism sector: A systematic literature review. Heliyon, 10(11), e31590. doi:10.1016/j.heliyon.2024.e31590.
[41] Torres-Moraga, E., Rodriguez-Sanchez, C., Alonso-Dos-Santos, M., & Vidal, A. (2024). Tourscape role in tourist destination sustainability: A path towards revisit. Journal of Destination Marketing & Management, 31, 100863. doi:10.1016/j.jdmm.2024.100863.

Publication Start Year:	2015
JCR 2023 Impact Factor (IF):	4.3
JIF QUARTILE:	Q1
SJR 2023 Quartile:	Q1
Acceptance Rate:	27%
Review Speed (Average):	76 days
Issue Per Year:	12
Number of Volumes:	10
Number of Issues:	107
Number of Articles:	1552
Number of Reviewers:	3417
Number of Contributors:	3604
Contributing Countries:	86
No. of WoS Citations:	7986
WoS h-index:	34
Scopus CiteScore:	7.0
No. of Google Citations:	17324
Google h-index:	47
Google i10-index:	393
Abstract Views:	5,819,908
PDF Download:	3,790,637

Modeling Tourist Transportation Mode Choice and Trip Chains Through Key Influencing Factors

Authors

Downloads

Login

Publisher and Affiliated Societies

Publisher & Affiliated Societies

Indexing & Abstracting

SidebarMenu

Journal Imprint

Journal Imprint

Journal Metrics

IndexedBy

Indexed In

Follow us

Follow Us

Most Cited Articles

Heavy Metal Removal Investigation in Conventional Activated Sludge Systems

Modeling of the Solar Thermal Energy Use in Urban Areas

Adsorption Behavior of Heavy Metal Ions by Hybrid Inulin-TEOS for Water Treatment

Modeling Finned Thermal Collector Construction Nanofluid-based Al2O3 to Enhance Photovoltaic Performance

visitors

Analytics

Information

Address

Contact Info: