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

Liu, Bing; Ma, Xiaolei; Tan, Erlong; Ma, Zhenliang

Liu, Bing; Ma, Xiaolei; Tan, Erlong; Ma, Zhenliang.

Afiliación

Liu B; School of Transportation Science and Engineering, Beihang University, Beijing 100191, People's Republic of China.
Ma X; School of Transportation Science and Engineering, Beihang University, Beijing 100191, People's Republic of China.
Tan E; Key Laboratory of Intelligent Transportation Technology and System, Ministry of Education, Beijing 100191, People's Republic of China.
Ma Z; School of Transportation Science and Engineering, Beihang University, Beijing 100191, People's Republic of China.

Philos Trans A Math Phys Eng Sci ; 381(2254): 20220253, 2023 Sep 04.

Article en En | MEDLINE | ID: mdl-37454692

Palabras clave

GCNinformer model; Gaussian naive Bayes model; anomaly detection; deep learning model; passenger flow; spatialtemporal dependencies

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Texto completo: 1 Bases de datos: MEDLINE Tipo de estudio: Diagnostic_studies / Prognostic_studies Idioma: En Revista: Philos Trans A Math Phys Eng Sci Asunto de la revista: BIOFISICA / ENGENHARIA BIOMEDICA Año: 2023 Tipo del documento: Article

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