Open AccessResearch on Passenger Throughput Forecast of Civil Aviation Airport Based on Multi-source Data
Author(s) -
Peng Daoyue,
Meng Zhang,
Yitian Xiao,
Yidan Wang
Publication year - 2022
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2179/1/012027
Subject(s) - civil aviation , aviation , throughput , regression analysis , population , linear regression , transport engineering , competition (biology) , computer science , international airport , research object , construct (python library) , gravity model of trade , operations research , engineering , geography , business , telecommunications , regional science , machine learning , aerospace engineering , international trade , ecology , demography , sociology , biology , programming language , wireless
In order to scientifically predict the passenger throughput of civil aviation airports. The paper taked Hefei Xinqiao International Airport as the research object, comprehensively considers multi-source data such as GDP, permanent population, airport competition, and spatial distance, used multiple linear regression, gravity-regression and other methods to construct a prediction model, and the results were obtained by different methods. The prediction result errors were compared and analyzed. The results of the study show GDP and population are important factors affecting the passenger throughput of civil aviation airports. The multiple linear regression model that considers the factors affecting airport competition can improve the accuracy of prediction, and the gravity-regression model can effectively predict the passenger throughput of civil aviation airports.