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

Wang, Bo; Ye, Mao; Zhu, Zhenya; Li, Yan; Liang, Qiangsheng; Zhang, Jian

doi:10.1109/access.2020.3024224

Cited by 7 publications

(5 citation statements)

References 35 publications

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“…With respect to input data, Ouyang [ 21 ] proposed using historical data for an LSTM encoder, and Jia [ 22 ] used subway automated toll data for a hybrid LSTM-stacked autoencoder. Wang [ 23 ] proposed an optimal passenger flow information input algorithm (OPFIIA), which sets parameters according to the predicted passenger flow information attributes of the target station and the correlation coefficient distribution characteristics of different stages, and selects the initial time correlation matrix. The experimental results show that by combining the three prediction methods of Adam-LSTM, Elman neural network, and ARIMA, OPFIIA can effectively select the best passenger flow input message matrix, and improve the training efficiency and the prediction accuracy of traditional prediction models.…”

Section: Literature Reviewmentioning

confidence: 99%

Forecasting metro rail transit passenger flow with multiple-attention deep neural networks and surrounding vehicle detection devices

Wang

2023

Appl Intell

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In the rapid development of public transportation led, the traffic flow prediction has become one of the most crucial issues, especially estimating the number of passengers using the Mass Rapid Transit (MRT) system. In general, predicting the passenger flow of traffic is a time-series problem that requires external information to improve accuracy. Because many MRT passengers take cars or buses to MRT stations, this study used external information from vehicle detection (VD) devices to improve the prediction of passenger flow. This study proposed a deep learning architecture, called a multiple-attention deep neural network (MADNN) model, based on historical MRT passenger flow and the flow from surrounding VD devices that estimates the weights of the vehicle detection devices. The model consists of (1) an MRT attention layer (MRT-AL) that generate hidden features for MRT stations, (2) a surrounding VD (SVD) attention layer (SVD-AL) that generate hidden features for SVD devices, and (3) an MRT-SVD attention layer (MRT-SVD-AL) that generate attention weights for each VD device in an MRT station. The results of the investigation indicated that the MADNN model outperformed the models without multiple-attention mechanisms in predicting the passenger flow of MRT traffic.

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Section: Literature Reviewmentioning

confidence: 99%

Forecasting metro rail transit passenger flow with multiple-attention deep neural networks and surrounding vehicle detection devices

Wang

2023

Appl Intell

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“…The performance of the system is evaluated using root means squared (RMS) error of 9.402%. The prediction of passenger frequency is optimized through the learning network with Adam's long and short-term memory network (Adam-LSTM) in urban rail transit [33]. The system ensured optimal prediction with a performance of 7.14% Mean Relative Error (MRE).…”

Section: Related Workmentioning

confidence: 99%

Intelligent Passenger Frequency Prediction for Transportation Sustainability using Kalman Filter Algorithm and Convolutional Neural Network

Jimoh¹,

Ajao²,

Adeleke³

et al. 2022

Preprint

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The passenger prediction flow is very significant to transportation sustainability. This is due to some chaos of traffic jams encountered by the road users during their movement to the offices, schools, or markets at earlier of the days and during closing periods. This problem is peculiar to the transportation system of the Federal University of Technology Minna, Nigeria. However, the prevailing technique of passenger flow estimation is non-parametric which depends on the fixed planning and is easily affected by noise. In this research, we proposed the development of a hybrid intelligent passenger frequency prediction model using the Auto-Regressive Integrated Moving Average (ARIMA) linear model, Convolutional Neural Network (CNN), and Kalman Filter Algorithm (KFA). The passengers’ frequency of arrival at the bus terminals is obtained and enumerated through the closed-circuit television (CCTV) and demonstrated using the Markovian Queueing Systems Model (MQSM). The ARIMA model was used for learning and prediction and compared the result with the combined techniques of using CNN-KFA. The autocorrelation coefficient functions (ACF) and partial autocorrelation coefficient functions (PACF) are used to examine the stationary data with different features. The performance of the models was analyzed and evaluated in describing the short-term passenger flow frequency at each terminal using the Mean Absolute Percentage Error (MAPE) and Mean Squared Error (MSE) values. The CNN-Kalman-filter model was fitted into the short-term series and the MAPE values are below 10%. The Mean Square Error (MSE) shows that the CNN-Kalman Filter model has the overall best performance with 83.33% of the time better than the ARIMA model and provides high accuracy in forecasting.

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“…Li et al [14] adopted the method of smart card data mining to analyze commuting characteristics and spatial distribution of origin-destination travel demand for different categories of stations. Wang et al [15] proposed a learning network based on the optimal passenger flow input information algorithm 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 were set reasonably.…”

Section: Related Workmentioning

confidence: 99%

Public Norms in the Operation Scheme of Urban Rail Transit Express Trains: The Case of the Beijing Changping Line

et al. 2021

Self Cite

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The development of urban rail transit is of great significance to the sustainable development of cities. However, the formulation of public norms of the urban rail transit train operation scheme lacks the corresponding theoretical support, rationality and scientificity. Therefore, based on the research on passenger flow conditions, this paper establishes an optimization model of express train operation and uses a heuristic genetic algorithm to solve it. In addition, this paper takes the Beijing Changping Line as an example to conduct empirical research. Results show that the optimization model established reduces the operation cost of operating enterprises, improves the capacity of urban rail transit, reduces travel time and maintains the full load rate of trains at a more comfortable level. This will improve the attractiveness of urban rail transit and promote benign interaction between operators and passengers. It will also provide a theoretical basis for formulating public norms of transit train operation, make it more scientific and reasonable and promote the development of urban rail transit.

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

Cited by 7 publications

References 35 publications

Forecasting metro rail transit passenger flow with multiple-attention deep neural networks and surrounding vehicle detection devices

Forecasting metro rail transit passenger flow with multiple-attention deep neural networks and surrounding vehicle detection devices

Intelligent Passenger Frequency Prediction for Transportation Sustainability using Kalman Filter Algorithm and Convolutional Neural Network

Public Norms in the Operation Scheme of Urban Rail Transit Express Trains: The Case of the Beijing Changping Line

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