Volume 23 Issue 6
Dec.  2023
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Article Navigation >  Journal of Traffic and Transportation Engineering  > 2023  >  23(6): 301-313
CHAI Lin-guo, RUI Tao, SHANGGUAN Wei, CAI Bai-gen. Group airport passengers travel recommendation method based on secondary induction[J]. Journal of Traffic and Transportation Engineering, 2023, 23(6): 301-313. doi: 10.19818/j.cnki.1671-1637.2023.06.020
Citation: CHAI Lin-guo, RUI Tao, SHANGGUAN Wei, CAI Bai-gen. Group airport passengers travel recommendation method based on secondary induction[J]. Journal of Traffic and Transportation Engineering, 2023, 23(6): 301-313. doi: 10.19818/j.cnki.1671-1637.2023.06.020
shu

Group airport passengers travel recommendation method based on secondary induction

doi: 10.19818/j.cnki.1671-1637.2023.06.020
  • 1.

    School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing 100044, China

  • 2.

    State Key Laboratory of Advanced Rail Autonomous Operation, Beijing Jiaotong University, Beijing 100044, China

  • 3.

    Wuhan Branch of China Railway Major Bridge Reconnaissance and Design Institute Co., Ltd., Wuhan 430073, Hubei, China

Funds:

National Key Research and Development Program of China 2018YFB1601200

More Information
  • Author Bio:

    CHAI Lin-guo(1988-), male, associate professor, PhD, lgchai@bjtu.edu.cn

  • Received Date: 2023-06-25
  • Publish Date: 2023-12-25
    Abstract
  • In view of the personalized travel needs of passengers and the requirements of rapid airport evacuation, a travel recommendation method for group airport passengers based on the secondary induction was proposed on the basis of the fixed allocation of each travel mode of landside transportation, so as to provide algorithmic support for customized passenger services. Based on the original passenger data, and combined with the rough set theory, the knowledge reduction of feature attributes was carried out to improve the performance of the algorithm. The improved Bayesian classification algorithm was used to quantify the travel mode recommendation degree based on the calculation of the independent feature probability of passengers, and the passenger travel recommendation sequence based on the primary induction was generated. In view of the constraint of fixed capacity allocation of each travel mode on the landside of the airport, the passenger travel recommendation sequence was input into the secondary-induced travel recommendation model of passengers based on the improved non-dominated sorting genetic algorithm (NSGA-Ⅱ) to deeply match the transport capacity and passenger flow, and the passenger travel recommendation results were optimized again. Based on the principle of universality, the small-scale (100 people) and large-scale (1 000 people) passenger samples were used for model validation. Analysis results show that good results can be obtained under the inputs of passenger flows with different scales. The correct rate of passenger travel mode recommendation in the small-scale sample is 77.41%. Under the large-scale sample, the correct rate of passenger travel mode recommendation is 79.62%. After the secondary induction, the matching degree between the recommended travel distribution of passenger flow and the transport capacity greatly improves compared with the real travel and the primary induction distribution. On the basis of high matching between the passenger flow and the transport capacity, the passenger travel preference needs are realized. The algorithm has good performance and provides a practical method to improve the passenger flow evacuation of hub airports.

     

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