GMAT-DU: Traffic Anomaly Prediction With Fine Spatiotemporal Granularity in Sparse Data

Zhao, Shuai; Zhao, Daxing; Liu, Ruiqiang; Xia, Zhengyuan; Cheng, Bo; Chen, Junliang

doi:10.1109/tits.2023.3249409

Cited by 3 publications

(4 citation statements)

References 34 publications

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“…In the work [15] , the temporal convolutional operation of ASTGCN was replaced with a Gate recurrent unit (a variation of RNN) module equipped with self-attention. Since then, several variant models [5], [7][8][9], [16] that combine GCN with RNN and incorporate attention mechanisms have been widely utilized in TFP.…”

Section: Related Work 21 Traffic Flow Predictionmentioning

confidence: 99%

“…FedSTN [8] 30.54 20.14 24.31 15.03 GMAT [9] 29.87 Next, we explore the impact of prediction horizon on the performance of TFP. Fig.…”

Section: Comparisons With Baselinesmentioning

confidence: 99%

“…However, these methods often face the issue of pattern loss caused by the fusion process of temporal and spatial features. Although many recent studies attempt to incorporate the attention mechanisms to avoid such losses, the variants of CNN and RNN still face drawbacks in learning long-term traffic trends [7][8][9] . Therefore, we propose a novel TFP model, called TPformer, which is based on the Transformer [10] .…”

Section: Introductionmentioning

confidence: 99%

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TPformer: a deep graphic transformer for long-term traffic flow prediction from node perspective

Jiang,

Li,

Liu

2024

Seventh International Conference on Traffic Engineering and Transportation System (ICTETS 2023)

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Long-term traffic flow prediction plays a crucial role in intelligent transportation systems. However, the temporal nonstationary nature of data from traffic detector nodes and the interdependence among nodes pose significant challenges in this prediction task. Therefore, we propose a traffic prediction transformer framework, called TPformer, to learn the spatiotemporal correlations of traffic features among traffic detector nodes. The success of applying the transformer to traffic flow prediction relies on the following key factors: 1) The length of both the input sequence and the predicted sequence is equal to the number of detector nodes, facilitating the construction of a node-node attention mechanism. 2) A node triplet encoding is introduced to allocate additional local contextual structural features to each node. 3) The topological structural features of the transportation network are incorporated into the self-attention mechanism, allowing the model to focus more on neighboring nodes when encoding the node traffic feature. Experimental results on two real-world datasets from the Performance Measurement System (PeMS) demonstrate that the proposed model outperforms state-of-the-art baselines in both short-term and long-term prediction tasks.

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Section: Related Work 21 Traffic Flow Predictionmentioning

confidence: 99%

“…FedSTN [8] 30.54 20.14 24.31 15.03 GMAT [9] 29.87 Next, we explore the impact of prediction horizon on the performance of TFP. Fig.…”

Section: Comparisons With Baselinesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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TPformer: a deep graphic transformer for long-term traffic flow prediction from node perspective

Jiang,

Li,

Liu

2024

Seventh International Conference on Traffic Engineering and Transportation System (ICTETS 2023)

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“…By learning the relationships between sensors, we could detect anomalies from sensors data [3][4][5] . However, traffic anomalies usually exhibit complex forms due to two aspects: high dimensionality, sparsity, abnormal scarcity (i.e., the need to correlate time and space, including speed or flow), and difficulty in capturing the hidden relationship between nodes (i.e., spatial modeling in the face of different data sources with varying degrees of anomalies in density or distribution and scale) 6,7 . Therefore, it is important to explore ways to capture complex inter-sensor relationships and detect anomalies from node relationships.…”

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

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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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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Traffic Anomaly Prediction based on Spatio-Temporal Uncertainty

Feng,

Piao,

Liu

et al. 2024

2024 39th Youth Academic Annual Conference of Chinese Association of Automation (YAC)

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GMAT-DU: Traffic Anomaly Prediction With Fine Spatiotemporal Granularity in Sparse Data

Cited by 3 publications

References 34 publications

TPformer: a deep graphic transformer for long-term traffic flow prediction from node perspective

TPformer: a deep graphic transformer for long-term traffic flow prediction from node perspective

Graph autoencoder with mirror temporal convolutional networks for traffic anomaly detection

Traffic Anomaly Prediction based on Spatio-Temporal Uncertainty

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