A Bayesian-based study on the average waiting time of airport taxis

Abstract

In recent years, the incompatibility of airport taxis with passenger time, scale and other factors has led to the problem that airport taxi traffic is difficult to control. This topic starts from the perspective of the number of taxis in Zhengzhou Airport, uses Bayesian statistics and R implementation, applies Bayesian calculation to the analysis of the average waiting time of airport taxis, and establishes the Poisson distribution model of the actual airport taxi traffic flow and Exponential distribution model for waiting time. This topic clearly presents the influence of factors such as the number of passengers and the number of taxis on the average waiting time of taxis. The visual description of the airport taxi waiting time is realized by using R language. By analyzing the model from multiple angles, the model is solved and tested with data, and the model is evaluated and improved after the end. The results show that the taxi traffic flow Indeed, it obeys the Poisson distribution model with η as the parameter, and the average taxi waiting time obeys the exponential distribution model with λ as the parameter, and the posterior distributions of the two are gamma distribution and inverse gamma distribution respectively, which is the city taxi. Management provides basic theoretical tools.