Research of Air Traffic Flow Forecasts Based on BP Neural Network

Chen, M.; Feng, Jie

doi:10.4028/www.scientific.net/amr.671-674.2912

Cited by 1 publication

(1 citation statement)

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“…In a way, it is one of the best traffic flow prediction models [66,67]. In the study of time series problems such as traffic flow prediction, the prediction accuracy of the BPNN can often reach more than 90%, whether it is a long-term or short-term prediction [6,14,29,68].…”

Section: Based On Intelligent Algorithmic Modelsmentioning

confidence: 99%

An Overview of Demand Analysis and Forecasting Algorithms for the Flow of Checked Baggage among Departing Passengers

Jiang,

Ding,

et al. 2024

Algorithms

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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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Section: Based On Intelligent Algorithmic Modelsmentioning

confidence: 99%