Short Term Passenger Flow Forecast of Metro Based on Inbound Passenger Plow and Deep Learning

Li, Yizhen; Yin, Ming; Zhu, Kuiyu

doi:10.1109/cisce52179.2021.9446016

Cited by 6 publications

(4 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Section: Discussionmentioning

confidence: 99%

“…The traditional passenger-flow prediction model achieves an effect similar to the passenger-flow characteristics by adjusting the neural network parameters [20,21]. EEMD is an improved algorithm for EMD that is prone to modal aliasing and can avoid modal aliasing by adding Gaussian white noise [26]; due to its unique memory forgetting function, LSTM has an advantage over RNN and ARIMA models in dealing with long text data [20]. Although EMD is a flexible and adaptive time-frequency data analysis method and performs good analysis and interpretation effects on nonlinear or non-stationary noise, it also has some defects: EMD does not consider the noise in the original signal that will interfere in actual conditions, so to adopt EMD to decompose signals with noise will give rise to modal aliasing, that is, signals of the same scale or frequency are divided into multiple eigenfunctions.…”

Section: Discussionmentioning

confidence: 99%

“…The prediction accuracy is compared with the existing research on EMD-LSTM [29] by using AFC data of the Dalian metro. Moreover, most existing studies employ static prediction, that is, using the same size of the training set to predict [18][19][20]. By changing the size of the training set to achieve the effect of dynamic prediction, EEMD-LSTM is used to compare dynamic and static prediction accuracy.…”

Section: Discussionmentioning

confidence: 99%

“…Zhang et al [19] used LSTM to predict short-term passenger flow through clustering to capture the characteristics and trends of passenger flow, and evaluated the reasonable time granularity interval. Li et al [20] proposed a prediction model that combines inbound passenger flow characteristics with LSTM by extracting the characteristics affecting the accuracy of prediction. Some scholars improved the LSTM neural network.…”

Section: Literature Reviewmentioning

confidence: 99%

See 3 more Smart Citations

Short-Term Forecast of OD Passenger Flow Based on Ensemble Empirical Mode Decomposition

2022

View full text Add to dashboard Cite

The development of metro systems can be a good solution to many problems in urban transport and promote sustainable urban development. A metro system plays an important role in urban public transit, and the passenger-flow forecasting is fundamental to assisting operators in establishing an intelligent transport system (ITS). In order to accurately predict the passenger flow of urban metros in different periods and provide a scientific basis for schedule planning, a short-term metro passenger-flow prediction model is constructed by integrating ensemble empirical mode decomposition (EEMD) and long short-term memory neural network (LSTM) to solve the problem that the existing empirical mode decomposition (EMD) is prone to modal aliasing. According to the processed metro-card data, the time series of historical OD data of metro passenger flow is obtained. After EEMD modal decomposition, several intrinsic mode functions sub-items and residual items are obtained. Then, an LSTM network is constructed for prediction. The time step of the network is decided according to the partial autocorrelation functions. The prediction results of intrinsic mode function (IMF) and residual items are integrated to obtain prediction results. The station is classified according to the land types around the station, and the model is tested by using the metro automatic fare collection (AFC) data. In order to test the actual prediction, a different number of training set samples are selected to predict. The measured data of the next day is continuously added to the original training set to compare the prediction accuracy. The results show that the mean absolute percentage error (MAPE) and root mean square error (RMSE) of the EEMD-LSTM model are better than the EMD-LSTM in predicting the OD value of commercial–residential stations and scenic–residential stations. Compared with the EMD-LSTM model, the EEMD-LSTM model showed an average reduction by 3.112% in MAPE values and 15.889 in RMSE, indicating that the EEMD-LSTM has higher prediction accuracy, and EEMD-LSTM model has higher accuracy in short-term metro passenger-flow prediction. The average MAPE for the 35-to-42-day historical data sample decreased from 13.02% to 10.39% with a decreasing trend. It shows that the prediction accuracy keeps improving as the training set samples increase.

show abstract