SPDNet: A Real‐Time Passenger Detection Method Based on Attention Mechanism in Subway Station Scenes

Yang, Jun; Zheng, Ying; Yan, Kunping; Liu, Hongjiang; Jin, Kun; Fan, WenLin; Han, Xiao; Zhang, YaWen

doi:10.1155/2021/7978644

Cited by 7 publications

(4 citation statements)

References 35 publications

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“…Te combination of the two completes the detection of passengers on and of the train. Yang et al [25] introduced CBAM into YOLOv4 to solve the problem of inhomogeneous illumination in the station to improve the accuracy and robustness of the network. Te MPDNet proposed by Yang et al [26] uses the pyramid vision transformer to extract features and then uses an adaptive spatial feature fusion algorithm to compensate for the loss of spatial information in feature extraction, achieving higher accuracy while meeting real-time requirements.…”

Section: Introductionmentioning

confidence: 99%

Subway Platform Passenger Flow Counting Algorithm Based on Feature-Enhanced Pyramid and Mixed Attention

Zuo,

Liu,

2023

Journal of Advanced Transportation

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Accurate access to real-time passenger flows on subway platforms helps to refine management in the era of networked operations. The narrow subway platforms suffer from significant crowd scale discrepancies and complex backgrounds when counting passenger flow. In the proposed passenger flow counting algorithm, the feature-enhanced pyramid structure is used to retain the channel information of deep features and eliminate the aliasing effect caused by fusion to enhance the feature representation of the original image and effectively solve the scale problem. The mixed attention mechanism suppresses background interference by capturing the global context relationship and focusing on the target area. On the ShanghaiTech Part_A dataset, the mean absolute error (MAE) and mean square error (MSE) of the proposed algorithm are 2.3% and 1.4% higher than those of the comparison algorithm, respectively. The MAE and MSE on the self-built platform dataset reach 3.1 and 5.7, respectively. The experimental results show that the accuracy of the proposed algorithm is improved and can meet the counting requirements of the subway platform scene.

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Section: Introductionmentioning

confidence: 99%

Subway Platform Passenger Flow Counting Algorithm Based on Feature-Enhanced Pyramid and Mixed Attention

Zuo,

Liu,

2023

Journal of Advanced Transportation

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“…e existing information platform establishes a channel of communication between the metro system and passengers to provide passengers with metro network status prediction information and help passengers plan their trips reasonably. At the same time, OD passenger flow prediction combined with real-time detection of passengers in densely crowded areas of metro stations [5] can provide better services for ensuring operational safety. However, the prediction results may differ from the actual situation, especially during peak periods.…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal Virtual Graph Convolution Network for Key Origin-Destination Flow Prediction in Metro System

Yang

Han

et al. 2022

Mathematical Problems in Engineering

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Short-term Origin-Destination (OD) flow prediction plays a major part in the realization of Smart Metro. It can help traffic managers implement dynamic control strategies to improve operation safety. Also, it can assist passengers in making reasonable travel plans to improve the passenger experience. However, there are problems that the dimension of OD short-term traffic prediction is much higher than the base number of metro stations and the OD matrix is sparse. To resolve the above two problems, a threshold-based method is proposed to extract key OD pairs first. OD passenger flow contains the attribute information of the Origin-Destination station and exhibits similar time evolution characteristics, so the spatial and temporal correlation needs to be considered in the prediction. Pearson correlation matrix is used to build a virtual graph and model the virtual connection between OD pairs. A spatiotemporal virtual graph convolutional network (ST-VGCN), which combines the advantages of a graph neural network and gated recurrent neural network, is proposed to identify spatial associations and temporal patterns simultaneously. The proposed method is evaluated on 39 days of real-world data from Shenzhen Metro, which outperforms other benchmarks. The research in this work can contribute to the development of short-term OD flow forecasts and help to provide ideas for the research on real-time operation and management of rail transit. Furthermore, it can help to establish passenger flow prediction and early warning mechanisms to quickly evacuate a large number of passengers in case of emergency.

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“…e intelligent construction of the metro is an important means to relieve the pressure of urban tra c, and train schedule optimization is one of the important ones [4]. In the metro system, passenger origin-destination (OD) information is very important.…”

Section: Introductionmentioning

confidence: 99%

Optimization of Metro Trains Operation Plans Based on Passenger Flow Data Analysis

Yang

Tang

et al. 2022

Mathematical Problems in Engineering

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Metro intelligent system produces massive passenger flow and traffic data every day, among which route, station, and operation data are important for optimizing the train operation scheme. We collect passenger flow information of Shenzhen metro, analyze the passenger flow pattern and its distribution characteristics based on the data warehouse of the Hadoop platform, and optimize the train operation scheme in this paper. Using dynamic passenger flow data, an optimization model with train departure and dwell time as decision variables and passenger waiting time, passenger ride time, train full load ratio, and train operation balance as objectives is developed. An improved parallel genetic algorithm (GA) incorporating a simulated annealing algorithm (SAA) and an optimal individual retention strategy is used to find the optimal result. To verify the usefulness of the method, simulation experiments are conducted on the optimization model and method using the real passenger flow and train operation data of Shenzhen metro, and the simulation results are compared with the original plan.

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SPDNet: A Real‐Time Passenger Detection Method Based on Attention Mechanism in Subway Station Scenes

Cited by 7 publications

References 35 publications

Subway Platform Passenger Flow Counting Algorithm Based on Feature-Enhanced Pyramid and Mixed Attention

Subway Platform Passenger Flow Counting Algorithm Based on Feature-Enhanced Pyramid and Mixed Attention

Spatiotemporal Virtual Graph Convolution Network for Key Origin-Destination Flow Prediction in Metro System

Optimization of Metro Trains Operation Plans Based on Passenger Flow Data Analysis

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