Abstract
Affected by the combination of different factors, taxi drivers leaving the airport will have different decision-making schemes. This paper establishes mathematical models to optimize the decision of airport taxi drivers and the optimization of passenger-carrying problems between taxis and passengers. First take Shuangliu Airport as an example, and take the number of taxis and the number of passengers in the data into the RBF neural network decision model. The results obtained are compared with the real data. Finally, the cyclic test shows the changes in taxi driver decisions is more dependent on the travel season, number of flights, and holidays. Then a taxi passenger model based on M/M/S is established. There are two arrangements for the two parallel lanes of the airport “ride zone”: the final solution is concluded that when the length of the boarding area is 50 meters, setting three “boarding points” has the highest total boarding efficiency. By introducing the average mileage division index of taxi drivers, the short- and long-haul taxi drivers are finally obtained after the optimized scheme. The returns have basically reached equilibrium.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.