Planning Above the API Clouds Before Flying Above the Clouds: A Real-Time Personalized Air Travel Planning Approach

  • Zelin Liu
  • Jian CaoEmail author
  • Yudong Tan
  • Quanwu Xiao
  • Mukesh Prasad


The rapid growth of the airline industry has resulted in the availability of a large number of flights, however this can also create a paralyzing problem. Flight information on all airlines across the world can be obtained via the Internet. Today, passengers trend to be interested in user personalized service. How to effectively find a passenger’s most preferred air travel plan, which might include multiple transfers from millions of possible choices with certain constraints, such as time and price, is a critical challenge. This paper presents an efficient air travel planning approach, which can find a number of air travel plans by invoking the APIs offered by airline companies. At the same time, these plans also best match the customer’s preference based on an analysis of historical orders. An algorithm to extract user preference features is introduced and heuristic rules to speed up the K path search process under constraints are presented. The experiment results show that the proposed model finds optimal air travel plans efficiently on a real-world dataset.


Air travel planning Personalized recommendation Network heuristic search 



This work is partially supported by National Key Research and Development Plan (No. 2018YFB1003800).


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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Zelin Liu
    • 1
  • Jian Cao
    • 1
    Email author
  • Yudong Tan
    • 2
  • Quanwu Xiao
    • 2
  • Mukesh Prasad
    • 3
  1. 1.Shanghai Jiao Tong UniversityShanghaiChina
  2. International, Ltd.ShanghaiChina
  3. 3.University of TechnologySydneyAustralia

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