Metro passenger flow forecasting though multi-source time-series fusion: An ensemble deep learning approach.

The increasing free access of the Internet provides us with favorable circumstances to investigate search engine index reflecting more and more personal behavior information. Part of valuable travel search information can assist us to achieve more robust and reliable prediction of metro passenger flow. Inspired by this, the paper develops a new multi-source time series fusion and direct interval prediction approach to grasp the dynamic law of metro passenger flow effectively. Multi-source index regarding metro travel from three major search engines (Baidu, Sogou and 360) in China are screened out and fused into the powerful predictors. By integrating an optimized multivariate mode decomposition strategy and long short-term memory model, lower and upper bounds of prediction interval are estimated directly by a multi-objective framework that combines the advantages of both the deep learning models long short-term memory and the ensemble learning approach. Especially, two sets of real experiment data of Beijing and Shanghai metro systems are employed to test our approach. Findings show that fusion of multi-source index information promotes the predictability of metro passenger flow, contributing to improving operation management and service quality. [Display omitted] • Fusing multi-source search data from three search engines based on query popularity. • Developing a new multivariate preprocessing technique MOHHOMVMD. • Proposing a direct multi-objective interval prediction approach LSTM-based LUBE. • Verifying the superiority of our approach over six benchmark models in two cases. [ABSTRACT FROM AUTHOR]