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.

The effect of COVID-19 on garlic prices

This paper studied the impact of COVID-19 on garlic price and found a model with high accuracy to predict garlic price to provide reference for relevant personnel in the garlic industry. Through the analysis of the average weekly price of garlic over the years, and analysis of garlic prices at specific time points since the outbreak in 2020.It was found that the outbreak had a relatively large impact on garlic prices，which kept garlic prices low relative to previous years. In order to better respond to emergencies.Therefore, it is particularly important to find a better forecasting model for garlic price prediction. It can provide a reference for people engaged in garlic industry.The CEEDMAN-LSTM combined model is used to forecast the average weekly garlic price in 2020, and the prediction results show that the model is suitable for the prediction of garlic price. © 2021 IEEE.