Design and Improvement of a Large Language Model Travel Planning System

Zhongsheng Sun

doi:10.54254/2755-2721/2025.BJ25286

Applied and Computational EngineeringOpen access

Design and Improvement of a Large Language Model Travel Planning System

Research Article

Open Access

Design and Improvement of a Large Language Model Travel Planning System

Zhongsheng Sun ^1*

¹ Shanghai Experimental Foreign Language School, Shanghai, China, 200000

^*Corresponding author: wuxie050817@gmail.com

Published on 20 July 2025

ACE Vol.177

ISSN (Print): 2755-273X

ISSN (Online): 2755-2721

ISBN (Print): 978-1-80590-241-6

ISBN (Online): 978-1-80590-242-3

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Abstract

With the rapid advancement and widespread adoption of large language models (LLMs), an increasing number of individuals have incorporated these models into their daily routines. To address persistent challenges in travel planning, this paper proposes an LLM-powered specialized travel planning system. By processing concise yet essential user inputs, the system can generate comprehensive travel plans tailored to individual requirements. This study designs user input templates and evaluates the response quality of the large language models Deepseek and Kimi. The responses are assessed and compared using predefined metrics to establish a preliminary system framework and identify limitations, such as insufficient information input, the computational complexity of evaluation metrics, and inefficient and deficient information output. This paper provides potential solutions that include API (application programming interface ) integration, enhanced user-system interaction, optimized prompt engineering, and implementation of advanced algorithms. This paper systematically reviews the system design, identifies prevailing challenges, and outlines future development trajectories. It validates the feasibility and potential of the Large Language Model Travel Planning System while establishing a comprehensive framework for LLM-based planning research, serving as a valuable reference for researchers in related fields.

Keywords:

Large Language Model, travel planning, API, prompts for LLMs

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References

[1]. Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. (2019, June). Bert: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of the 2019 conference of the North American chapter of the association for computational linguistics: human language technologies, volume 1 (long and short papers) (pp. 4171-4186).

[2]. Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., Dhariwal, P., ... & Amodei, D. (2020). Language models are few-shot learners. Advances in neural information processing systems, 33, 1877-1901.

[3]. Chen, A., Ge, X., Fu, Z., Xiao, Y., & Chen, J. (2024). TravelAgent: An AI assistant for personalized travel planning. arXiv preprint arXiv: 2409.08069.

[4]. Turnip, F. F., & Turnip, A. (2020, June). Development of Online Ticket Booking Application for Ferry Crossing Website Based in Toba Lake Area. In 2020 3rd International Conference on Mechanical, Electronics, Computer, and Industrial Technology (MECnIT) (pp. 381-385). IEEE.

[5]. Shao, J. J., Yang, X. W., Zhang, B. W., Chen, B., Wei, W. D., Guo, L. Z., & Li, Y. F. (2024). ChinaTravel: A Real-World Benchmark for Language Agents in Chinese Travel Planning. arXiv preprint arXiv: 2412.13682.

References

[3]. Chen, A., Ge, X., Fu, Z., Xiao, Y., & Chen, J. (2024). TravelAgent: An AI assistant for personalized travel planning. arXiv preprint arXiv: 2409.08069.

Cite this article

Sun,Z. (2025). Design and Improvement of a Large Language Model Travel Planning System. Applied and Computational Engineering,177,21-25.

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-MLA 2025 Symposium: Applied Artificial Intelligence Research

ISBN: 978-1-80590-241-6(Print) / 978-1-80590-242-3(Online)

Editor: Hisham AbouGrad

Conference website: https://2025.confmla.org/

Conference date: 3 September 2025

Series: Applied and Computational Engineering

Volume number: Vol.177

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

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