Flight time table-based real-time large-scale aviation hub parking lot scale prediction method

Abstract

A flight time table-based real-time large-scale aviation hub parking lot scale prediction method comprises the steps of determining research objects including passengers, parking lots and travel modes; generating a passenger arrival and departure time statistical table; determining the proportion of the passenger travel modes under different classification modes, wherein the proportion is determined according to flight types or travel purposes or travel ages; obtaining travel proportion results under three different classification modes, and selecting the optimal passenger classification; respectively determining the average passenger number, the parking ratio and the parking turnover rate of the taxi and the private car; establishing a parking lot queuing system model; parking lot queuingsystem model solving. The real-time large-scale aviation hub parking lot scale prediction method based on the flight time table provides basic research support for large-scale aviation hub transportation organization planning and scheduling, and provides a specific optimization method.

Description

technical field [0001] The invention relates to a method for predicting the scale of an aviation hub parking lot. In particular, it involves a real-time large-scale aviation hub parking lot size prediction method based on flight schedules Background technique [0002] Large-scale aviation hub airports are the nodes of the central route network and the distribution center of air passenger and cargo, so the scale of passenger throughput is quite large. In 2017, Beijing Capital Airport's passenger throughput exceeded 100 million, an increase of nearly 25% compared to 2015. The huge passenger throughput has brought huge motor vehicle traffic to the hub airport. How to guide passengers has become a major problem in aviation hubs. [0003] In an airport hub, passengers can use a variety of transportation methods to arrive at or leave the airport, such as private cars, taxis, rental cars, airport shuttles, chartered cars, buses, trains, subways, MRT vehicles, and even helicopter...

AI Technical Summary

Problems solved by technology

This method regards the surveyed data as the average traffic volume, which is not dynamic and real-time

[0005] In addition, some scholars have studied the prediction of airport traffic volume based on flight schedules, but they have not studied flight types, flight delays, etc., and most of the research is applied to the planning and design of aviation hub traffic organization, and rarely applied to traffic coordination and scheduling.

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