Prediction of High Speed Railway Passenger Demand Volume Based on Grey Relational Analysis

Rong, Wenyu; Liu, Di; He, X. T.

doi:10.1061/9780784479384.023

Cited by 3 publications

(1 citation statement)

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“…His model obtains more accurate results compared to time series. Moreover, the application of the grey relational analysis to forecast the passenger demand for high-speed railway was studied by Rong et al (2015). Another interesting application of grey models is a study by Zhang and Qin (2017), where forecasting the demand for Chinese ski tourism is studied.…”

Section: Other Forecasting Methodsmentioning

confidence: 99%

A review of demand forecasting models and methodological developments within tourism and passenger transportation industry

Ghalehkhondabi

Ardjmand

Young

et al. 2019

JTF

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Purpose The purpose of this paper is to review the current literature in the field of tourism demand forecasting. Design/methodology/approach Published papers in the high quality journals are studied and categorized based their used forecasting method. Findings There is no forecasting method which can develop the best forecasts for all of the problems. Combined forecasting methods are providing better forecasts in comparison to the traditional forecasting methods. Originality/value This paper reviews the available literature from 2007 to 2017. There is not such a review available in the literature.

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Section: Other Forecasting Methodsmentioning

confidence: 99%

A review of demand forecasting models and methodological developments within tourism and passenger transportation industry

Ghalehkhondabi

Ardjmand

Young

et al. 2019

JTF

View full text Add to dashboard Cite

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Multivariable forecasting approach of high‐speed railway passenger demand based on residual term of Baidu search index and error correction

Li,

Sun

et al. 2024

Journal of Forecasting

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Accurate prior information of passenger flow demand on high‐speed railway is of great significance for the operation and the management of transportation systems. Various factors in modern social life have caused uncertainty at demand. Recently, individuals are increasingly depending on the online search results when choosing among different transportation modes, services, and destinations, which provide important basic information for forecasting the travel demand. This study employs Baidu search index to assist in capturing volatility of high‐speed railway passenger demands, offering insights into the travel inclinations and travelers' actions. Furthermore, we have given more in‐depth attention and analysis to their residual term accounting for the random nature caused by various factors. To this end, a sophisticated deep analysis mechanism based on data decomposition has been devised to extract and analyze the valuable information concealed within the residuals, so as to enhance the comprehension of the variability inherent in the high‐speed railway passenger flow. Meanwhile, an error correction strategy is implemented for all residual terms to improve further their forecasting accuracy. Experimental results from two real‐world datasets demonstrate the effectiveness and robustness of the developed hybrid approach across several popular evaluation indicators. Therefore, this study can function as a reliable instrument, provide sensible data‐driven guidance for resource allocation and make scientific decisions in the railway industry.

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High-speed rail passenger flow prediction based on crossformer and quantile regression: A deep learning approach assisted by internet search indices

Xie,

Zhang,

et al. 2025

Measurement

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Prediction of High Speed Railway Passenger Demand Volume Based on Grey Relational Analysis

Cited by 3 publications

References 0 publications

A review of demand forecasting models and methodological developments within tourism and passenger transportation industry

A review of demand forecasting models and methodological developments within tourism and passenger transportation industry

Multivariable forecasting approach of high‐speed railway passenger demand based on residual term of Baidu search index and error correction

High-speed rail passenger flow prediction based on crossformer and quantile regression: A deep learning approach assisted by internet search indices

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