Journal of Guangxi Normal University(Natural Science Edition) ›› 2024, Vol. 42 ›› Issue (4): 41-50.doi: 10.16088/j.issn.1001-6600.2023070103

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Scheduling Research of Airport Passengers Remote Shuttle System Based on Modular Vehicle Technology

HU Yucong1*, FENG Qilu1, HE Kezhi2, GONG Tailin1   

  1. 1. School of Civil Engineering and Transportation, South China University of Technology, Guangzhou Guangdong 510641, China;
    2. Department of Industrial Engineering, Tsinghua University, Beijing 100084, China
  • Received:2023-07-01 Revised:2023-09-05 Online:2024-07-25 Published:2024-09-05

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Abstract: In order to improve the level of remote parking passenger shuttle service at airports, an autonomous remote shuttle system using modular vehicle technology is constructed, with the objective of minimizing the total economic cost for passengers and operators, while considering the constraints of system safety, number of people conservation and operation quality, a dispatching model is constructed with unequal departure time by using a commercial solver Gurobi. To improve the speed in model-solving, a genetic algorithm is also designed, and the speed and accuracy in model-solving of the two methods are compared and analyzed. The results show that the departure solution by Gurobi solver is optimal, and the genetic algorithm obtains an approximate solution with a difference of only 3.4%, but the computation time required by the genetic algorithm is 1% of that of Gurobi; and the total cost with a small difference can basically meet the actual operation needs in terms of the quality of the solution. In addition, by comparing with the shuttle bus departure scheme, it is found that the total cost of the departure scheme based on modular vehicle technology is reduced by 64.50% and the average passenger waiting time is reduced by 76.35%, which proves that the scheme has better economy than the traditional scheme and can significantly improve the service level of the system.

Key words: modular vehicle, remote shuttle, dispatching, Gurobi solution, genetic algorithm

CLC Number:  V35;U492.22
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