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

Abstract

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.