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• W4285310669 abstract "Traffic patterns of urban road intersections are important in traffic monitoring and accident prediction, thus play crucial roles in urban traffic management. Although real-time traffic information is consistently provided by surveillance cameras equipped at road intersections, the sparsity of surveillance distribution poses great challenges in performing a complete real-time traffic pattern analysis. To tackle that, existing works either assume that the traffic patterns are static, or assume a multi-variant distribution model for intersection traffic volumes. The former assumption neglects the temporal features of traffic patterns, and the latter is limited in capturing fine-grained spatiotemporal dependencies. To tackle the problem, we propose a novel framework, SpatioTemporal-Generative Adversarial Network (ST-GAN), that exploits deep spatiotemporal features of urban networks and offers accurate traffic pattern inferences with incomplete surveillance information. The ST-GAN framework incorporates a modified GCN network wired with the encoder-decoder mechanism and an LSTM network, which are further boosted by an iterative adversarial training process. Comprehensive experiments on real datasets show that ST-GAN achieves better inference accuracies than state-of-the-art solutions." @default.
• W4285310669 created "2022-07-14" @default.
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• W4285310669 date "2022-09-01" @default.
• W4285310669 modified "2023-10-16" @default.
• W4285310669 title "Inferring Intersection Traffic Patterns With Sparse Video Surveillance Information: An ST-GAN Method" @default.
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• W4285310669 doi "https://doi.org/10.1109/tvt.2022.3176653" @default.
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