Correction: Empirical mode decomposition based long short-term memory neural network forecasting model for the short-term metro passenger flow.

[This corrects the article DOI: 10.1371/journal.pone.0222365.].

Empirical mode decomposition based long short-term memory neural network forecasting model for the short-term metro passenger flow.

Short-term metro passenger flow forecasting is an essential component of intelligent transportation systems (ITS) and can be applied to optimize the passenger flow organization of a station and offer data support for metro passenger flow early warning and system management. LSTM neural networks have recently achieved remarkable recent in the field of natural language processing (NLP) because they are well suited for learning from experience to predict time series. For this purpose, we propose an empirical mode decomposition (EMD)-based long short-term memory (LSTM) neural network model for predicting short-term metro inbound passenger flow. The EMD algorithm decomposes the original sequential passenger flow into several intrinsic mode functions (IMFs) and a residual. Selected IMFs that are strongly correlated with the original data can be obtained via feature selection. The selected IMFs and the original data are integrated into inputs for LSTM neural networks, and a single LSTM prediction model and an EMD-LSTM hybrid forecasting model are developed. Finally, historical real automatic fare collection (AFC) data from metro passengers are collected from Chengdu Metro to verify the validity of the proposed EMD-LSTM prediction model. The results indicate that the proposed EMD-LSTM hybrid forecasting model outperforms the LSTM, ARIMA and BPN models.

Surface-enhanced fluorescence from silver fractallike nanostructures decorated with silver nanoparticles.

Fluorescence emission of fluorophore molecules in the close vicinity of a nanostructured metal surface can be enhanced through a local electromagnetic field with the help of surface plasmon resonance. The fluorescence enhancement effect is very sensitive to the topography and dielectric property of the metal substrate. In the current work, metal substrates with complex structures, which are made of silver fractallike structures and nanoparticles (NPs), are prepared through electrochemical reduction followed by physical deposition. The surface-enhanced fluorescence of Rhodamine 6G monolayer molecules deposited on the prepared complex substrates are investigated with the laser spectroscopic technique. The experimental results show that the fractallike structure decorated with silver NPs presents stronger fluorescence enhancement, compared with silver NPs or pure silver fractallike structures.

Surface-enhanced fluorescence of rhodamine 6G on the assembled silver nanostructures.

The fluorescence emission and enhancement of Rhodamine 6G on the silver island films (SiFs) with and without 4-Aminothiophenol molecules have been investigated. It was found that the SiFs without 4-Aminothiophenol molecules (PATP) have less contribution to the fluorescence enhancement. However, when PATP molecules as a spacer were deposited on the SiFs, obvious fluorescence enhanced effect was observed. The results show that the fluorescence enhancement depends on the morphology of the substrate surface and the distance between fluorophore molecules and the substrate surface. The experimental observations can be explained by local surface plasmon resonance and local field enhancement theory.