离境旅客托运行李流量需求分析和预测算法概述

Algorithms Pub Date : 2024-04-23 DOI:10.3390/a17050173
Bo Jiang, Guofu Ding, Jianlin Fu, Jian Zhang, Yong Zhang
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引用次数: 0

摘要

行李流研究对于实现机场服务资源的高效、智能分配和调度具有举足轻重的作用,同时也是决定机场行李处理系统的设计、开发和流程优化的基本要素。本文研究机场离港旅客托运的行李。首先回顾并分析了行李流需求的研究现状。然后,通过客观数据实例得出结论:虽然机场旅客流量与行李流量之间存在显著的相关性,但旅客流量的增加并不一定会导致行李流量的成比例增加。根据现有的行李流量影响因素梳理和分类研究成果,行李流量的主要影响因素分为宏观影响因素和微观影响因素两类。在研究经济与行李流之间的关系时,建议采用包含多种经济指标的综合分析方法,而不是单纯依赖 GDP。本文简要概述了目前流行的运输流量预测方法,将算法模型分为三类:基于数理统计模型的算法模型、基于智能算法的算法模型和利用人工神经网络的组合算法模型。分析了各种交通流预测算法的结构、优缺点及其应用场景。阐述了在行李流量预测中使用基于人工神经网络的组合预测模型的潜在优势。最后对行李流量需求研究进行了展望。本综述可为机场管理和行李处理系统开发方面的学者提供进一步的研究帮助。
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An Overview of Demand Analysis and Forecasting Algorithms for the Flow of Checked Baggage among Departing Passengers
The research on baggage flow plays a pivotal role in achieving the efficient and intelligent allocation and scheduling of airport service resources, as well as serving as a fundamental element in determining the design, development, and process optimization of airport baggage handling systems. This paper examines baggage checked in by departing passengers at airports. The crrent state of the research on baggage flow demand is first reviewed and analyzed. Then, using examples of objective data, it is concluded that while there is a significant correlation between airport passenger flow and baggage flow, an increase in passenger flow does not necessarily result in a proportional increase in baggage flow. According to the existing research results on the influencing factors of baggage flow sorting and classification, the main influencing factors of baggage flow are divided into two categories: macro-influencing factors and micro-influencing factors. When studying the relationship between the economy and baggage flow, it is recommended to use a comprehensive analysis that includes multiple economic indicators, rather than relying solely on GDP. This paper provides a brief overview of prevalent transportation flow prediction methods, categorizing algorithmic models into three groups: based on mathematical and statistical models, intelligent algorithmic-based models, and combined algorithmic models utilizing artificial neural networks. The structures, strengths, and weaknesses of various transportation flow prediction algorithms are analyzed, as well as their application scenarios. The potential advantages of using artificial neural network-based combined prediction models for baggage flow forecasting are explained. It concludes with an outlook on research regarding the demand for baggage flow. This review may provide further research assistance to scholars in airport management and baggage handling system development.
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