CEEMDAN-IPSO-LSTM: A Novel Model for Short-Term Passenger Flow Prediction in Urban Rail Transit Systems.

Urban rail transit (URT) is a key mode of public transport, which serves for greatest user demand. Short-term passenger flow prediction aims to improve management validity and avoid extravagance of public transport resources. In order to anticipate passenger flow for URT, managing nonlinearity, correlation, and periodicity of data series in a single model is difficult. This paper offers a short-term passenger flow prediction combination model based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and long-short term memory neural network (LSTM) in order to more accurately anticipate the short-period passenger flow of URT. In the meantime, the hyperparameters of LSTM were calculated using the improved particle swarm optimization (IPSO). First, CEEMDAN-IPSO-LSTM model performed the CEEMDAN decomposition of passenger flow data and obtained uncoupled intrinsic mode functions and a residual sequence after removing noisy data. Second, we built a CEEMDAN-IPSO-LSTM passenger flow prediction model for each decomposed component and extracted prediction values. Third, the experimental results showed that compared with the single LSTM model, CEEMDAN-IPSO-LSTM model reduced by 40 persons/35 persons, 44 persons/35 persons, 37 persons/31 persons, and 46.89%/35.1% in SD, RMSE, MAE, and MAPE, and increase by 2.32%/3.63% and 2.19%/1.67% in R and R2, respectively. This model can reduce the risks of public health security due to excessive crowding of passengers (especially in the period of COVID-19), as well as reduce the negative impact on the environment through the optimization of traffic flows, and develop low-carbon transportation.

Novel quantitative structure-activity relationship model to predict activities of natural products against COVID-19.

Currently, COVID-19 is spreading in a large scale while no efficient vaccine has been produced. A high-effective drug for COVID-19 is very necessary now. We established a satisfied quantitative structure-activity relationship model by gene expression programming to predict the IC50 value of natural compounds. A total of 27 natural products were optimized by heuristic method in CODESSA program to build a liner model. Based on it, only two descriptors were selected and utilized to build a nonlinear model in gene expression programming. The square of correlation coefficient and s2 of heuristic method were 0.80 and 0.10, respectively. In gene expression programming, the square of correlation coefficient and mean square error for training set were 0.91 and 0.04. The square of correlation coefficient and mean square error for test set are 0.86 and 0.1. This nonlinear model has stronger predictive ability to develop the targeted drugs of COVID-19.