LightGBM‐based model for metro passenger volume forecasting

Driven by the era of big data and the development of artificial intelligence, potential traffic patterns can be obtained by analysing the numerous data. Metro has become an essential transport infrastructure and the passenger volume provides the basic support for the optimisation of the metro system. Thus, accurate forecasting of the volume is extremely required. In this study, a model for improving the accuracy and stability of metro passenger volume prediction named VMD-TPE-LightGBM (light gradient boosting machine) is proposed. The original dataset is firstly regrouped both in the station and chronological order while the time interval is reset as 10-minute. Time features for extracting the hidden patterns are extracted by analysing the variation tendency of the passenger volume. For enhancing the precision, the variational mode decomposition algorithm is applied to decompose the original data series. Then each of the modes is regarded as the input of the LightGBM model, which are optimised by a tuning method named the tree of Parzen estimators and K-fold cross-validation. According to this process, the final forecasting results are acquired by reconstructing the predicted modes. The experimental results demonstrate that the proposed model performs superior to all the comparisons and has an impressive effect on short-term metro passenger volume forecasting.