Research on Ride-hailing User Travel Preference Identification and Personalized Recommendation Strategies Based on Machine Learning

Xiaotong Shi

doi:10.54254/2755-2721/2025.GL27135

Applied and Computational EngineeringOpen access

Research on Ride-hailing User Travel Preference Identification and Personalized Recommendation Strategies Based on Machine Learning

Research Article

Open Access

Research on Ride-hailing User Travel Preference Identification and Personalized Recommendation Strategies Based on Machine Learning

Xiaotong Shi ^1*

¹ Columbia University

^*Corresponding author: john33361@gmail.com

Published on 24 September 2025

ACE Vol.186

ISSN (Print): 2755-273X

ISSN (Online): 2755-2721

ISBN (Print): 978-1-80590-383-3

ISBN (Online): 978-1-80590-384-0

Download Cover

Abstract

The rapid expansion of ride-hailing services has generated massive amounts of user travel data, presenting both opportunities and challenges for service optimization. This research proposes a comprehensive framework for identifying user travel preferences and developing personalized recommendation strategies using advanced machine learning techniques. Our methodology integrates feature extraction algorithms, pattern recognition models, and recommendation systems to enhance user experience and operational efficiency. Through extensive experiments on real-world datasets, we demonstrate that our approach achieves 87.3% accuracy in preference identification and improves user satisfaction by 23.7% compared to conventional methods. The proposed framework effectively addresses the heterogeneity of user behaviors while maintaining computational efficiency, providing practical solutions for ride-hailing platforms to deliver customized services and optimize resource allocation.

Keywords:

travel preference identification, personalized recommendation, machine learning, ride-hailing services

View PDF

References

[1]. de la Torre, R., Corlu, C. G., Faulin, J., Onggo, B. S., & Juan, A. A. (2021). Simulation, optimization, and machine learning in sustainable transportation systems: Models and applications. Sustainability, 13(3), 1551.

[2]. Lai, Z., Wang, J., Zheng, J., Ding, Y., Wang, C., & Zhang, H. (2023). Travel mode choice prediction based on personalized recommendation model. IET Intelligent Transport Systems, 17(4), 667-677.

[3]. Gupta, B. B., Gaurav, A., Marín, E. C., & Alhalabi, W. (2022). Novel graph-based machine learning technique to secure smart vehicles in intelligent transportation systems. IEEE transactions on intelligent transportation systems, 24(8), 8483-8491.

[4]. Long, W., Li, T., Xiao, Z., Wang, D., Zhang, R., Regan, A. C., ... & Zhu, Y. (2022). Location prediction for individual vehicles via exploiting travel regularity and preference. IEEE Transactions on Vehicular Technology, 71(5), 4718-4732.

[5]. Ali, F., Sarwar, S., Shafi, Q. M., Iqbal, M., Safyan, M., & Qayyum, Z. U. (2022). Securing IoT based maritime transportation system through entropy-based dual-stack machine learning framework. IEEE Transactions on Intelligent Transportation Systems, 24(2), 2482-2491.

[6]. Yuan, T., da Rocha Neto, W., Rothenberg, C. E., Obraczka, K., Barakat, C., & Turletti, T. (2022). Machine learning for next‐generation intelligent transportation systems: A survey. Transactions on emerging telecommunications technologies, 33(4), e4427.

[7]. Tsolaki, K., Vafeiadis, T., Nizamis, A., Ioannidis, D., & Tzovaras, D. (2023). Utilizing machine learning on freight transportation and logistics applications: A review. ICT Express, 9(3), 284-295.

[8]. Jian, W., He, J., Chen, K., Xie, J., Zhao, J., & You, L. (2024). Personalized travel recommendations based on asynchronous and privacy-preserving mixed logit model. IEEE Transactions on Intelligent Transportation Systems.

[9]. Afolayan, B. I., Ghosh, A., Calderin, J. F., & Arredondo, A. D. M. (2024). Emerging Trends in Machine Learning Assisted Optimization Techniques Across Intelligent Transportation Systems. IEEE Access.

[10]. Ang, K. L. M., Seng, J. K. P., Ngharamike, E., & Ijemaru, G. K. (2022). Emerging technologies for smart cities' transportation: geo-information, data analytics and machine learning approaches. ISPRS International Journal of Geo-Information, 11(2), 85.

[11]. Alqahtani, H., & Kumar, G. (2024). Machine learning for enhancing transportation security: A comprehensive analysis of electric and flying vehicle systems. Engineering Applications of Artificial Intelligence, 129, 107667.

[12]. Nitu, P., Coelho, J., & Madiraju, P. (2021). Improvising personalized travel recommendation system with recency effects. Big Data Mining and Analytics, 4(3), 139-154.

[13]. Mou, J., Gao, K., Duan, P., Li, J., Garg, A., & Sharma, R. (2022). A machine learning approach for energy-efficient intelligent transportation scheduling problem in a real-world dynamic circumstances. IEEE transactions on intelligent transportation systems, 24(12), 15527-15539.

[14]. Aghamohammadghasem, M., Azucena, J., Hashemian, F., Liao, H., Zhang, S., & Nachtmann, H. (2023, December). System simulation and machine learning-based maintenance optimization for an inland waterway transportation system. In 2023 Winter simulation conference (WSC) (pp. 267-278). IEEE.

[15]. Wang, J., Wu, N., & Zhao, W. X. (2021). Personalized route recommendation with neural network enhanced search algorithm. IEEE Transactions on Knowledge and Data Engineering, 34(12), 5910-5924.

References

[2]. Lai, Z., Wang, J., Zheng, J., Ding, Y., Wang, C., & Zhang, H. (2023). Travel mode choice prediction based on personalized recommendation model. IET Intelligent Transport Systems, 17(4), 667-677.

[12]. Nitu, P., Coelho, J., & Madiraju, P. (2021). Improvising personalized travel recommendation system with recency effects. Big Data Mining and Analytics, 4(3), 139-154.

[15]. Wang, J., Wu, N., & Zhao, W. X. (2021). Personalized route recommendation with neural network enhanced search algorithm. IEEE Transactions on Knowledge and Data Engineering, 34(12), 5910-5924.

Cite this article

Shi,X. (2025). Research on Ride-hailing User Travel Preference Identification and Personalized Recommendation Strategies Based on Machine Learning. Applied and Computational Engineering,186,39-51.

Data availability

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

About volume

Volume title: Proceedings of CONF-FMCE 2025 Symposium: Semantic Communication for Media Compression and Transmission

ISBN: 978-1-80590-383-3(Print) / 978-1-80590-384-0(Online)

Editor: Anil Fernando

Conference website: https://2025.conffmce.org/glasgow.html

Conference date: 24 October 2025

Series: Applied and Computational Engineering

Volume number: Vol.186

ISSN: 2755-2721(Print) / 2755-273X(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).