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.
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