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Accurate short-time passenger volume prediction can guarantee the efficient scheduling command of urban rail transit. However, the short-time passenger flow of rail transit has the characteristics of nonlinearity and high randomness. In order to improve the prediction accuracy of short-time passenger volume, the Seasonal-Trend decomposition using Loess (STL) method and Human Evolutionary Optimization Algorithm (HEOA) is employed to optimize the bi-directional long short-term memory neural network (BiLSTM). Thus, a combined STL-HEOA-BiLSTM prediction model is proposed. Firstly, the inbound passenger volume along the urban rail transit is classified according to the Pearson correlation number. Secondly, the STL algorithm decomposes different types of short-time passenger flow data into Trend component (Tt), Seasonal component (St) and Residual component (Rt). Thirdly, the HEOA optimizes the various types of hyper-parameters of the BiLSTM model. Finally, the optimized BiLSTM model predicts Tt, St and Rt individually, and the final prediction value is obtained based on the combination of the three predictions. Three evaluation metrics are used to assess the results and quantify the effectiveness of the combined model. The example analysis shows that the prediction accuracy of the combined STL-HEOA-BiLSTM model surpasses that of the other six common and combined models in forecasting short-duration passenger flow. This experimental result shows the effectiveness, accuracy and applicability of the STL-HEOA-BiLSTM model proposed in this paper. It is demonstrated that the proposed model has a reference value for urban rail transit operators. [ABSTRACT FROM AUTHOR]

The metro system is indispensable for alleviating traffic congestion in the urban transportation system. Precise metro passenger flow (MPF) prediction is crucial in ensuring smooth operations of the metro system. Recently, the graph convolutional network (GCN), which is effective in the spatial feature extraction, has been applied in traffic prediction. However, most existing GCN-based methods construct the empirical graphs based on distance and adjacency, which cannot fully express the correlations of metro stations. This paper proposes a novel MPF prediction method consisting of three parts: K-means-based metro station functional clustering (KMSFC), external feature fusion, and dual-view recurrent GCN (DVRGCN). The KMSFC identifies the metro stations both having similar MPF changing tendencies and being located in similar urban functional areas. Furthermore, the DVRGCN is designed to simultaneously capture the spatiotemporal and external features. The dual-view GCN module in the DVRGCN captures both explicit and implicit spatial features of the metro traffic network. To demonstrate the capability for making accurate MPF predictions, the experiments using a real-world metro traffic dataset are conducted. The ablation experiments are also performed to prove the contribution of each module in the proposed method. The experimental results show that the proposed method outperforms other state-of-the-art traffic prediction methods. • Precise clustering of passenger travel patterns at different metro stations. • Comprehensive consideration of spatial and temporal features of metro networks. • Informative presentation of passenger flow volumes for different metro stations. • Accurate predictions of future passenger flows of all metro stations. [ABSTRACT FROM AUTHOR]

In this paper, we study the big data multi-strategy predator algorithm for tourist flow prediction and explore the application of the algorithm in optimizing the tourist flow prediction model to improve the prediction accuracy and efficiency. An adversarial learning strategy extends the search space, an adaptive weighting factor balances the global and local search ability, and a variance operation combined with differential evolution is used to avoid local optimal traps. The experiment adopts variables such as network booking volume and search index as inputs for passenger flow prediction. The predator algorithm is trained by Extreme Learning Machine (ELM) to optimize the input weights and biases to build the FMMPAELM model. The results show that on the training samples, the FMMPA-ELM model predictions are highly consistent with the actual values, with a maximum prediction index of 200.On the test samples, although there are errors, the FMMPA-ELM model exhibits better prediction ability than the traditional ELM model. It is concluded that the FMMPAELM model can effectively improve the accuracy of passenger flow prediction and provide powerful decision support for the tourism industry.