Forecasting air passenger traffic flow based on the two-phase learning model
Forecasting air passenger traffic flow based on the two-phase learning model
复制标题
基于两阶段学习模型的航空旅客流量预测
DOI：
10.1007/s11227-020-03428-2
复制
发表时间：
2020-09-22
影响因子：
3.3
通讯作者：
Zhou, Xinzhi
中科院分区：
文献类型：
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作者：
Wu, Xinfang;Xiang, Yong;Zhou, Xinzhi
未来的机场将朝着高度智能化的方向发展，如无人值机服务，而地面服务的规模和资源配置与航空客流密切相关。因此，准确的客流预测对未来机场的发展和民航服务的优化具有重要的影响。航空客流作为一种时间序列，受多种因素的影响，尤其是季节性、趋势性和波动性的随机部分。这将最终影响预测的准确性。因此，本文介绍了一种基于两阶段学习框架的预测模型。在第一阶段，不同的预测器并行科普时间序列的不同特征，预测结果在第二阶段被整合。将主要误差指标与实际数据进行比较，结果表明两阶段学习模型的性能优于现有融合模型，且性能稳定。
The future airports will head toward a highly intelligent direction, like the unmanned check-in services, while the scale and resources allocation of the ground service are tightly related to the air passenger flow. Therefore, forecasting passenger flow accurately will affect the development of future airports and the optimization of service of civil airlines significantly. As a kind of time series, air passenger flow is influenced by multiple factors, particularly, the stochastic part of seasonality, trend and volatility. These will ultimately affect the accuracy of the prediction. Therefore, this paper introduces a prediction model based on a two-phase learning framework. In phase one, various predictors cope with different features of time series in parallel and the prediction results are integrated in phase two. Furthermore, this paper has compared principal error indicators with actual data and results show that the two-phase learning model performs better than current fusion models and owns stable performance.