Railways Passengers Comfort Evaluation through Motion Parameters: A Systematic Review

Silva, Patricia; Ribeiro, Diogo; Mendes, Joaquim; Seabra, Eurico; Postolache, Octavian

doi:10.3390/machines11040465

Cited by 2 publications

(1 citation statement)

References 55 publications

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“…Factors from the macro level, the regional GDP level, industrial structure, the population in the airport radiation area, and other factors are closely related to the airport flow. Different relationships between different modes of transportation, such as aircraft transportation and railways, highways, and adjacent airports, will also affect the volume of airport traffic [14][15][16][17][18]. A higher airport density and developed road network traffic will affect airport passenger flow.…”

Section: Macro-factorsmentioning

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: Macro-factorsmentioning

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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Research on Check-In Baggage Flow Prediction for Airport Departure Passengers Based on Improved PSO-BP Neural Network Combination Model

Jiang,

Zhang,

et al. 2024

Aerospace

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Accurate forecasting of passenger checked baggage traffic is crucial for efficient and intelligent allocation and optimization of airport service resources. A systematic analysis of the influencing factors and prediction algorithms for the baggage flow is rarely included in existing studies. To accurately capture the trend of baggage flow, a combined PCC-PCA-PSO-BP baggage flow prediction model is proposed. This study applies the model to predict the departing passengers’ checked baggage flow at Chengdu Shuangliu International Airport in China. First, in the preprocessing of the data, multiple interpolation demonstrates a better numerical interpolation effect compared to mean interpolation, regression interpolation, and expectation maximization (EM) interpolation in cases of missing data. Second, in terms of the influencing factors, unlike factors that affect the airport passenger flow, the total retail sales of consumer goods have a weak relationship with the baggage flow. The departure passenger flow and flight takeoff and landing sorties play a dominant role in the baggage flow. The railway passenger flow, highway passenger flow, and months have statistically significant effects on the changes in the baggage flow. Factors such as holidays and weekends also contribute to the baggage flow alternation. Finally, the PCC-PCA-PSO-BP model is proposed for predicting the baggage flow. This model exhibits superior performance in terms of the network convergence speed and prediction accuracy compared to four other models: BP, PCA-BP, PSO-BP, and PCA-PSO-BP. This study provides a novel approach for predicting the flow of checked baggage for airport departure passengers.

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Railways Passengers Comfort Evaluation through Motion Parameters: A Systematic Review

Cited by 2 publications

References 55 publications

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

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

Research on Check-In Baggage Flow Prediction for Airport Departure Passengers Based on Improved PSO-BP Neural Network Combination Model

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