单航班行李提取旅客密度动态预测

taptap下载安装安卓学报 ›› 2020, Vol. 38 ›› Issue (5): 35-40.

单航班行李提取旅客密度动态预测

邢志伟¹，吴哲¹，罗谦²

1. taptap下载安装安卓电子信息与自动化学院，天津300300；2. 中国民用航空局第二研究所，成都610041

出版日期:2020-10-25 发布日期:2020-10-23
作者简介:邢志伟（1970—），男，辽宁沈阳人，教授，博士，研究方向为民航装备与系统、机场交通信息与控制.
基金资助:
国家重点研发计划资助项目（2018YFB1601200）

Dynamic passenger density prediction in baggage claim area for single flight

XING Zhiweⁱ¹, WU Zhe¹, LUO Qian²

1. College of Electronic Information and Automation, Civil Aviation University of China, Tianjin 300300, China;
2. The Second Research Institute of Civil Aviation administration of China, Chengdu 610041, China

Online:2020-10-25 Published:2020-10-23

摘要/Abstract

摘要： 针对行李提取区旅客拥挤问题，结合行李提取流程分析和历史数据机器学习，提出一种基于贝叶斯网络的单航班行李转盘旅客密度预测模型。利用贝叶斯网增量学习的特性实现模型的动态调整，使该模型可以对新数据更好适应和调整，并得出更准确地旅客密度预测值。使用国内某大型枢纽机场数据，采用期望最大化（EM）方法对模型进行训练。实验结果表明，所建模型能有效预测航班行李提取的旅客密度，具有较高的准确度。

关键词: 机场运行, 旅客密度, 行李提取, 贝叶斯网络, 动态预测

Abstract: A Bayesian network-based model is proposed to predict the density of passengers in the baggage carousel of a single flight, combining with baggage claiming process analysis and machine learning of historical data to establish Bayesian network. Incremental learning characteristics of Bayesian network is used to realize dynamic adjustment of BN model, so that it can better adapt the new data and get more accurate prediction of passenger density. Taking extracted data from a large hub airport in China as instance, expectation maximization (EM) method is used to conduct model training. Results show that the proposed model can effectively predict the density of passengers in baggage claim area with high accuracy.

Key words: airport operation, passenger density, baggage claim, Bayesian network, dynamic prediction

中图分类号:

邢志伟, 吴哲, 罗谦. 单航班行李提取旅客密度动态预测[J]. taptap下载安装安卓学报, 2020, 38(5): 35-40.

XING Zhiwei, WU Zhe, LUO Qian. Dynamic passenger density prediction in baggage claim area for single flight[J]. Journal of Civil Aviation University of China, 2020, 38(5): 35-40.

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