Perceiving spatiotemporal traffic anomalies from sparse representation-modeled city dynamics

Gao, Jun; Zheng, Daqing; Yang, Su

doi:10.1007/s00779-020-01474-4

Cited by 4 publications

(4 citation statements)

References 47 publications

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“…Moreover, there exists a category of networks, such as TCNs (temporal convolutional networks), designed to address temporal dependencies, which can capture global temporal information 11 . However, TCNs may not be as flexible in the context of traffic timing due to variations in the amount of historical information needed for model predictions OPEN 1 The College of Computer Science Chengdu University of Information Technology, Chengdu 610225, China. 2 Sichuan Digital Transportation Technology Co., Ltd, Chengdu, China.…”

Section: Graph Autoencoder With Mirror Temporal Convolutional Network...mentioning

confidence: 99%

“…There is a growing need for abnormal event detection in transportation networks. The Shanghai Bund trampling incident that occurred on December 31, 2014, in China is a widely known tragedy closely associated with traffic anomaly detection 1 . Furthermore, on January 26, 2017, in Harbin, the largest city in northeastern China, a single traffic incident resulted in a chain of rear-end collisions, leading to eight fatalities and thirty-two injuries 2 .…”

Section: Introductionmentioning

confidence: 99%

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Graph autoencoder with mirror temporal convolutional networks for traffic anomaly detection

Ren,

Li,

Peng

et al. 2024

Sci Rep

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Traffic time series anomaly detection has been intensively studied for years because of its potential applications in intelligent transportation. However, classical traffic anomaly detection methods often overlook the evolving dynamic associations between road network nodes, which leads to challenges in capturing the long-term temporal correlations, spatial characteristics, and abnormal node behaviors in datasets with high periodicity and trends, such as morning peak travel periods. In this paper, we propose a mirror temporal graph autoencoder (MTGAE) framework to explore anomalies and capture unseen nodes and the spatiotemporal correlation between nodes in the traffic network. Specifically, we propose the mirror temporal convolutional module to enhance feature extraction capabilities and capture hidden node-to-node features in the traffic network. Morever, we propose the graph convolutional gate recurrent unit cell (GCGRU CELL) module. This module uses Gaussian kernel functions to map data into a high-dimensional space, and enables the identification of anomalous information and potential anomalies within the complex interdependencies of the traffic network, based on prior knowledge and input data. We compared our work with several other advanced deep-learning anomaly detection models. Experimental results on the NYC dataset illustrate that our model works best compared to other models for traffic anomaly detection.

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Section: Graph Autoencoder With Mirror Temporal Convolutional Network...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Graph autoencoder with mirror temporal convolutional networks for traffic anomaly detection

Ren,

Li,

Peng

et al. 2024

Sci Rep

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“…Zhao et al [8] tackled the uncertainty of taxi demands and the impact of the parallel car-hailing markets (e.g., Uber demands) on taxi demands through a unified framework that can use multi-source data systematically. Gao et al [9] conducted sparse representation on taxi activity over spatially partitioned cells in a city. They can perceive the deviation from the normal evolution of traffic flows and find the traffic anomalies.…”

Section: Accepted Articlesmentioning

confidence: 99%

Active crowd sensing

Wang

Espada

2021

Pers Ubiquit Comput

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“…There is a growing need for abnormal event detection in transportation networks. A widely known tragedy closely related to traffic anomaly detection is the Shanghai Bund trampling incident that occurred on December 31, 2014, in China 1 . In addition, on January 26, 2017, in Harbin, the largest city in northeastern China, a single traffic incident caused a serial rear-end collision accident where eight people died, and thirty-two people were injured 2 .…”

Section: Introductionmentioning

confidence: 99%

MTGAE: Graph Autoencoder with Mirror TCN for Traffic Anomaly Detection

Ren

et al. 2023

Preprint

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Traffic time series anomaly detection has been intensively studied for years because of its potential applications in intelligent transportation. Classical traffic anomaly detection methods ignore the hidden dynamic associations between road network nodes as it evolves, resulting in the inability to capture the long-term temporal correlation, spatial and temporal characteristics of traffic data, and the abnormal nodes from it in a dataset with high periodicity and trends (e.g., morning peak travel). In this paper, we proposed a Mirror Temporal Graph Autoencoder (MTGAE) framework to explore anomalies and capture unseen nodes and the spatiotemporal correlation between nodes in the traffic network. In the method, we propose Mirror Temporal Convolutional Module (MTCM) module, to our best knowledge, which possesses better feature extraction and captures hidden node-to-node features in the traffic network and will be passed as hidden to the next module. Our proposed Graph Convolutional Gate Recurrent Unit Cell (GCGRU CELL) module, mapped to a high-dimensional space by Gaussian kernel functions, which discerns anomalous information in the traffic network and possible anomalies in the complex interdependencies between nodes and nodes based on prior knowledge and input data. We compared our work with several other advanced deep-learning anomaly detection models. The experimental results on the NYC dataset prove that our model works best compared to other models for traffic anomaly detection.

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Perceiving spatiotemporal traffic anomalies from sparse representation-modeled city dynamics

Cited by 4 publications

References 47 publications

Graph autoencoder with mirror temporal convolutional networks for traffic anomaly detection

Graph autoencoder with mirror temporal convolutional networks for traffic anomaly detection

Active crowd sensing

MTGAE: Graph Autoencoder with Mirror TCN for Traffic Anomaly Detection

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