RNN-Based Subway Passenger Flow Rolling Prediction

Sha, Shouwei; Li, Jing; Zhang, Ke; Yang, Zhen; Wei, Zijian; Li, Xueyan; Zhu, Xin

doi:10.1109/access.2020.2964680

Cited by 46 publications

(25 citation statements)

References 39 publications

(25 reference statements)

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“…We define two mode-accuracy measures. First, "mode_accuracy_1" (MA1) is derived as in (13), and is the probability that the RNN output mode is the same with the true label. ) × 100 (13) where | | is the number of test datasets; ( , ) is the identity operator that returns 1 when and are identical; otherwise, it returns 0.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…Many studies are being conducted on how to activate sensors around objects with duty-cycled activation to efficiently use energy. There is a problem in that maximizing the inactive period to save energy causes severe data transmission delays and prevents the network from performing proper object detection functions [12] [13]. In object tracking of WSNs, it is very important to dynamically determine the optimal sensing duty cycle.…”

Section: Introductionmentioning

confidence: 99%

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Recurrent Neural Network-Based Optimal Sensing Duty Cycle Control Method for Wireless Sensor Networks

Choi

Yoo

2021

IEEE Access

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With the development of Internet of Things (IoT) technologies, environmental monitoring systems using wireless sensor networks (WSNs) have received considerable attention. Reliable object detection and tracking is an important research issue in various WSN applications, such as environment and disaster monitoring, disaster propagation tracking, and intruder monitoring and tracking. Generally, because batteries are used as energy sources for sensors in WSNs, a highly energy-efficient operation is needed to prolong the life of the sensors and networks. To save energy, sensors usually manage multi-mode sensing operations, in which they periodically switch between active and inactive periods. A tradeoff exists between object detection accuracy and energy efficiency when we select a sensing schedule. Depending on the object speed, direction, and sensor deployment topology, different sensing schedules should be dynamically applied to individual sensors. In this paper, we propose a novel recurrent neural network (RNN)-based dynamic duty cycle control method for sensor nodes. For RNN training, a target optimal duty cycle for a given network condition is derived from the proposed digital twin-space analytic solution. Simulation results show that the proposed model provides accurate object detection performance and achieves high energy efficiency.INDEX TERMS Duty cycle control, machine learning, object tracking, recurrent neural network, wireless sensor networks.

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Section: Simulation Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Recurrent Neural Network-Based Optimal Sensing Duty Cycle Control Method for Wireless Sensor Networks

Choi

Yoo

2021

IEEE Access

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“…2 School of Computer, Electronics and Information, Guangxi University, Nanning 530004, China. 3 Guangxi Meteorological Information Centre, Nanning 530022, China.…”

Section: Ethics Approval and Consent To Participatementioning

confidence: 99%

“…With the application of deep learning more and more widely, many scholars began to use deep learning technology to study time series prediction. As a typical deep learning model, Recurrent neural network(RNN) has been applied in time series prediction [2][3][4].Compared with the traditional time series prediction methods, RNN improves the prediction accuracy, but it still has the shortcomings of gradient disappearance and gradient explosion. LSTM is an improved RNN, which overcomes the shortcomings of RNN.…”

Section: Introductionmentioning

confidence: 99%

A Deep Learning Method for ECG Signal Prediction Based on VMD, Cao Method, and LSTM Neural Network

Fu-ying

Qin

Wang

et al. 2021

Preprint

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Background: In body area network (BAN), accurate prediction of ECG signal can not only let doctors know the patient's condition in advance, but also help to reduce the energy consumption of sensors. In order to improve the accuracy of ECG signal prediction, this paper proposes a deep learning method for ECG signal prediction. Methods: The proposed prediction method combines variational mode decomposition (VMD), Cao method and a long short-term memory (LSTM) neural network. In the method, VMD decomposes ECG data into a series of intrinsic mode functions (IMFs), which reduces the non-stationary character of ECG signals and helps to improve the prediction accuracy. Cao method is used to determine the input dimension of LSTM input layer, namely, the minimum embedding dimension of each IMF is the input dimension of LSTM input layer. Each IMF is predicted by a LSTM neural network which adopts Adam optimizer. All IMFs predictions are aggregated to get the final prediction result. Results: To evaluate the prediction accuracy of the proposed method, simulation experiments are carried out on ECG data from the MIT-BIH Arrhythmia Database. Experimental results show that the RMSE (root mean square error) and MAE (mean absolute error) of the proposed model are 0.001326 and 0.001044 respectively, which are more than 10 percent lower than the traditional prediction methods.Conclusions: Compared with some traditional prediction methods, the proposed prediction method improves the prediction accuracy obviously.

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“…Hochreite et al [24] proposed a long and short-time memory network (LSTM), in which a definite error conveyor belt was introduced to solve the gradient explosion problem in BPTT. Recently, some improved algorithms of LSTM have also been applied to short-term passenger flow or other traffic prediction field, which has been proved to have high performance [25]- [29].…”

Section: Introductionmentioning

confidence: 99%

Short-Term Passenger Flow Prediction for Urban Rail Stations Using Learning Network Based on Optimal Passenger Flow Information Input Algorithm

Wang

Zhu

et al. 2020

IEEE Access

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Massive short-term passenger flow prediction models of urban rail transit stations have been used in different conditions. However, researchers encountered several challenges while selecting the optimal passenger flow information input matrix and eliminating the redundant information in the original data. In this paper, we propose a learning network based on the optimal passenger flow input information algorithm (MTFLN) method. Based on the passenger flow information attribute of the predicted target station and the correlation coefficient distribution characteristics in different stages, the parameters of the optimal passenger flow information input algorithm (OPFIIA) are set reasonably. The optimal passenger flow input information matrix is also determined. We combine the training results of MTFLN method to optimize parameters of the Adam optimization algorithm-long and short-time memory network (Adam-LSTM). In the case study, the short-term passenger flow predicted values of urban rail transit stations are presented by three predicted methods including Adam-LSTM, Elman neural network and autoregressive integrated moving average (ARIMA). The presented results demonstrate that selecting the initial time correlation matrix by combining the attributes of the predicted target station and using OPFIIA method can not only effectively select the optimal passenger flow input information matrix, but also improve training efficiency and the prediction accuracy of traditional predicted model. INDEX TERMS Urban rail transit, short-term passenger flow prediction, optimal passenger flow information input algorithm (OPIIA), Long and Short-time Memory Network (LSTM), multi-factors.

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RNN-Based Subway Passenger Flow Rolling Prediction

Cited by 46 publications

References 39 publications

Recurrent Neural Network-Based Optimal Sensing Duty Cycle Control Method for Wireless Sensor Networks

Recurrent Neural Network-Based Optimal Sensing Duty Cycle Control Method for Wireless Sensor Networks

A Deep Learning Method for ECG Signal Prediction Based on VMD, Cao Method, and LSTM Neural Network

Short-Term Passenger Flow Prediction for Urban Rail Stations Using Learning Network Based on Optimal Passenger Flow Information Input Algorithm

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