Study on air-rail intermodal ticketing optimization based on K-means clustering — A case study of Qingdao Jiaodong International Airport

Yuhan Sun

doi:10.54254/2977-3903/2025.22335

Research Article

Open access

Published on 16 April 2025

Download pdf

Sun,Y. (2025). Study on air-rail intermodal ticketing optimization based on K-means clustering — A case study of Qingdao Jiaodong International Airport. Advances in Engineering Innovation,16(4),18-23.

Export citation

Study on air-rail intermodal ticketing optimization based on K-means clustering — A case study of Qingdao Jiaodong International Airport

Yuhan Sun *^,1,

¹ Shandong University of Science and Technology

* Author to whom correspondence should be addressed.

https://doi.org/10.54254/2977-3903/2025.22335

Abstract

To improve the intermodal service at Qingdao Jiaodong Airport, addressing operational challenges such as fuzzy passenger demand layering and insufficient cross-modal coordination, and to solve the core issues of supply-demand mismatches and a single pricing mechanism in the air-rail intermodal ticketing system, this study proposes a personalized ticketing optimization strategy based on user profiling. First, through extensive survey data, the study analyzes the personal attributes and travel characteristics of the surveyed passengers. Then, using the K-means clustering algorithm, the study clusters passengers' multidimensional features and determines the optimal number of clusters through the elbow method and silhouette coefficient method. This leads to the establishment of differentiated user labels: economy-class passengers, business-class passengers, and leisure-class passengers. The market segmentation research on passenger groups shows that these three distinct groups perceive the bottlenecks of intermodal services differently, especially exhibiting significant layering features in the key dimensions of time sensitivity and price sensitivity. The results provide a comparative scheme for improving the air-rail intermodal ticketing service at Qingdao Jiaodong International Airport, offering differentiated service strategies for each passenger group. Through responsive demand and resource optimization, this study has significant practical implications for enhancing passenger experience and strengthening the market competitiveness of the service.

Keywords

intermodal transport, K-means clustering algorithm, ticketing optimization, user labels, Qingdao Jiaodong Airport

View pdf

References

[1]. Zhou, J. (2024). Along the railway: Qingdao reaches more "distant places." Qingdao Daily.

[2]. Yin, G., Liu, S., & Gao, P. (2017). A study on the connection model between Qingdao hub airport and high-speed rail. Shandong Traffic Science and Technology,(04), 90-92.

[3]. Gang, H., & Yan, J. (2022). A study on ticketing information sharing between multiple transport modes in intermodal transport. Traffic World, Z1, 5-6+16. https://doi.org/10.16248/j.cnki.11-3723/u.2022.z1.049

[4]. Zhang, H., Wang, B., Yang, F. Y., & Jiang, G. F. (2024). Passenger intermodal transport service pricing based on travel choice. Journal of Railway Science and Engineering, 46(11), 12-20.

[5]. Liu, X., & Yan, C. (2023). A railway-dominated passenger intermodal transport service system under the MaaS concept. Transportation Research, 9(03), 82-88+99. https://doi.org/10.16503/j.cnki.2095-9931.2023.03.009

[6]. Grison, E., Gyselinck, V., & Burkhardt, J. M. (2016). Exploring factors related to users' experience of public transport route choice: Influence of context and user profiles. Cognition, Technology & Work, 18(2), 287-301.

[7]. Moussa, S., Soui, M., & Abed, M. (2013). User profile and multi-criteria decision making: Personalization of traveler’s information in public transportation. Procedia Computer Science, 22, 411-420.

[8]. Zhang, Z. (2014). Research on local standards planning sequences based on cluster analysis. Traffic Standardization, 42(12), 169-172. https://doi.org/10.16503/j.cnki.2095-9931.2014.12.056

[9]. Yin, X. (2021). Traffic travel recommendation methods and applications based on passenger profiles and travel chain models (Doctoral dissertation, Beijing Jiaotong University).

[10]. Vijayan, H., M S, & K S. (2024). A-MKMC: An effective adaptive-based multilevel K-means clustering with optimal centroid selection using a hybrid heuristic approach for handling incomplete data. Data & Knowledge Engineering, 150, 102243. https://doi.org/10.1016/j.datak.2023.102243

[11]. Khan, I. K., Daud, H. B., Zainuddin, N. B., Sokkalingam, R., Farooq, M., Baig, M. E., Ayub, G., & Zafar, M. (2024). Determining the optimal number of clusters by enhanced gap statistic in K-means algorithm. Egyptian Informatics Journal, 27, 100504. https://doi.org/10.1016/j.eij.2024.100504

[12]. Williams, J. J. P., Jr, Hill, R. R., & Chicken, E. (2022). Wavelet analysis of variance box plot. Journal of Applied Statistics, 49(14), 3536-3563. https://doi.org/10.1080/02664763.2021.1951685

[13]. Carling, K. (2000). Resistant outlier rules and the non-Gaussian case. Computational Statistics & Data Analysis, 33(3), 249-258. https://doi.org/10.1016/S0167-9473(99)00057-2

Cite this article

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

Disclaimer/Publisher's Note

The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of EWA Publishing and/or the editor(s). EWA Publishing and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

About volume

Journal：Advances in Engineering Innovation

Volume number: Vol.16

ISSN：2977-3903(Print) / 2977-3911(Online)

© 2024 by the author(s). Licensee EWA Publishing, Oxford, UK. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license. Authors who publish this series agree to the following terms:
1. Authors retain copyright and grant the series right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this series.
2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the series's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this series.
3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See Open access policy for details).