Optimization Model of Airport Taxi Riding Point Based on Neural Network and Genetic Algorithm

Abstract

How to improve transportation has always been a major social issue, especially for airport traffic, How to improve transportation has always been a major social issue, especially for airport traffic, how to optimize the taxi ride point has a very important significance. Based on the analysis of the influence of the taxi density distribution, this paper gives the optimization scheme of the ride point, an improved multivariate decision model based on neural network was established, and the optimal ride point was obtained by traversing the decision variables with genetic algorithm. First of all, the density of taxis is studied qualitatively and quantitatively, and the multi-dimensional decision-making model based on the improved neural network is established. It was found that the model had the greatest dependence on population density and the least dependence on taxi distribution rate. Secondly, the genetic algorithm is used to traverse the decision variables to get the minimum total walking distance of passengers, that is the optimal ride point.