MVOPFAD: Multiview Online Passenger Flow Anomaly Detection-Reference-Cited by-同舟云学术

MVOPFAD: Multiview Online Passenger Flow Anomaly Detection

Published:2024-05-01 Issue:9 Volume:24 Page:14668-14681
ISSN:1530-437X
Container-title:IEEE Sensors Journal
language:
Short-container-title:IEEE Sensors J.

Author:

Tan Erlong¹^ORCID,Yan Haoyang¹^ORCID,Zhao Kaiqi¹,Ma Xiaolei¹^ORCID,Ma Zhenliang²^ORCID,Du Yuchuan³^ORCID

Affiliation:

1. School of Transportation Science and Engineering and the Key Laboratory of Intelligent Transportation Technology and System, Ministry of Education, Beihang University, Beijing, China

2. Department of Civil and Architectural Engineering, KTH Royal Institute of Technology, Stockholm, Sweden

3. Key Laboratory of Road and Traffic Engineering, Ministry of Education, Tongji University, Shanghai, China

Funder

National Key Research and Development Program of China

Beijing Nova Program

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/7361/10516472/10474301.pdf?arnumber=10474301

Reference51 articles.

1. Obstacle Detection for Intelligent Transportation Systems Using Deep Stacked Autoencoder and $k$ -Nearest Neighbor Scheme

2. Passenger flow anomaly detection in urban rail transit networks with graph convolution network–informer and Gaussian Bayes models

3. MRSA-Net: Anomaly Detection of Metro Stations Based on Residual Attention Network With Path Aggregation and Efficient Representation

4. Detecting metro service disruptions via large-scale vehicle location data

5. Enhancing urban metro system resilience under disruptive events through multi-agent reinforcement learning

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