Research On Forecast Technology Of Airport Taxi Capacity

The growth of the overall economy and the increase in the level of resident consumption have stimulated the rapid growth in the demand for air passenger transport.More popular aircraft travel modes have accelerated the increase in airport passenger throughput,which provides an important population base for the development of large airports.It also exerted greater pressure on the transportation capacity of the airport’s diversified modes of transportation.Taxi,as a kind of flexible vehicle,is convenient for the analysis of the number of passengers around the clock.In addition,the research on the demand forecasting technology of taxi capacity can be extended to the needs analysis of other modes of transport and provide reference for the demand for airport transportation capacity.Therefore,this paper will mainly focus on the analysis of the capacity requirements of taxis,and use the real-time statistical data of Capital Airport to test the effectiveness of the model.This paper first analyzes the data of the passenger flow counting statistics equipment and performs data cleaning,that is,re-reviews and checks the data collected by the passenger flow counting statistics equipment,finds and corrects errors in the data file.Then,this paper studies and selects the characteristics of airport taxi passenger flow model,including analyzing the law of airport taxi passenger flow,and selecting the features for machine learning that best characterize the capacity demand forecasting model from various forms of flight and passenger data.Models are used to forecast capacity requirements.In the research and design of subsequent capacity demand forecasting algorithms,this paper builds an appropriate model based on the characteristics of airport taxi passenger flow data to be analyzed,designs appropriate data mining algorithms,and predicts taxis capacity demand based on real-time information in current databases.Finally,the research results are verified.This paper simulates the simulation environment,and uses the passenger flow and capacity scheduling data collected by the Capital Airport to verify the algorithm’s accuracy and prediction efficiency.Based on the verification results,the prediction model is improved.The verification results show that the seasonality and trend removal algorithm based on the characteristics of passenger flow in airport taxis has better performance at various time points and can effectively predict the number of passengers in the waiting area.The taxi capacity obtained by the model can provide an early warning for the potential shortage of taxi capacity at the airport taxi dispatch center,allocate and dispatch taxi resources reasonably,solve the passenger detention,and improve the passenger flow capacity of the airport.