Airport Arrival Flow Prediction considering Meteorological Factors Based on Deep-Learning Methods

Zhao Yang,Yifan Wang,Yi Zhong,Jiyang Ma,Jie Li,Liming Liu,Min Xia

doi:10.1155/2020/6309272

Abstract

This study presents a combined Long Short-Term Memory and Extreme Gradient Boosting (LSTM-XGBoost) method for flight arrival flow prediction at the airport. Correlation analysis is conducted between the historic arrival flow and input features. The XGBoost method is applied to identify the relative importance of various variables. The historic time-series data of airport arrival flow and selected features are taken as input variables, and the subsequent flight arrival flow is the output variable. The model parameters are sequentially updated based on the recently collected data and the new predicting results. It is found that the prediction accuracy is greatly improved by incorporating the meteorological features. The data analysis results indicate that the developed method can characterize well the dynamics of the airport arrival flow, thereby providing satisfactory prediction results. The prediction performance is compared with benchmark methods including backpropagation neural network, LSTM neural network, support vector machine, gradient boosting regression tree, and XGBoost. The results show that the proposed LSTM-XGBoost model outperforms baseline and state-of-the-art neural network models.

Highlights

E prediction performance is compared with benchmark methods including backpropagation neural network, Long Short-Term Memory (LSTM) neural network, support vector machine, gradient boosting regression tree, and XGBoost. e results show that the proposed LSTMXGBoost model outperforms baseline and state-of-the-art neural network models
Introduction e airport is the terminal for aircraft taking off and landing. It is the transferring point for passenger distribution. e daily air traffic flow has strong periodicity and randomness. ere are many factors influencing the airport arrival flow, among which the most widely acknowledged are the complex meteorological factors, for example, the change of short-term arrival flow caused by severe weather such as thunderstorm in summer and blizzard in winter, as well as the unfavorable weather conditions that may affect visibility [1, 2]
Conclusions is paper proposed a combined Long Short-Term Memory and Extreme Gradient Boosting (LSTM-XGBoost) method for arrival flow prediction at the airport. e traditional Long Short-Term Memory (LSTM) network and the XGBoost model are incorporated by taking both the timeseries information and the meteorological features into account. e Pearson correlation coefficients are calculated to describe the strength of the linear correlation between two variables, and the importance of variables is identified. e prediction results are compared with some benchmark methods, including BP, LSTM, support vector machine (SVM), gradient boosting regression tree (GBRT), and XGBoost

Summary

Introduction

E prediction performance is compared with benchmark methods including backpropagation neural network, LSTM neural network, support vector machine, gradient boosting regression tree, and XGBoost. e results show that the proposed LSTMXGBoost model outperforms baseline and state-of-the-art neural network models. Erefore, it is necessary to take the meteorological factors into account when forecasting the short-term arrival flow at the airport. A series of studies have been conducted regarding the short-term traffic flow prediction based on timeseries data. Lu et al proposed a combined method for short-term highway traffic flow prediction based on a recurrent neural network [12]. Asadi and Regan presented a spatiotemporal decomposition-based deep neural network for time-series forecasting with the case of highway traffic flow data from. As compared with highway traffic flow prediction, the short-term prediction of airport arrival flow tends to be more complicated, due to the stochasticity and dynamic nature of air traffic flow considering the various influencing factors such as weather conditions [15,16,17]. Further development is still needed to advance the predictive aspects of the linkage between airport arrival flow and the input variables including meteorological variables and to predict future arrival flow using data mining techniques

Methods

Results

Conclusion