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Given historical traffic distributions and associated urban conditions observed in a city, the conditional urban traffic estimation problem aims at estimating realistic future projections of the traffic under a set of new urban conditions, e.g., new bus routes, rainfall intensity, and travel demands. The problem is important in reducing traffic congestion, improving public transportation efficiency, and facilitating urban planning. However, solving this problem is challenging due to the strong spatial dependencies of traffic patterns and the complex relations between the traffic and urban conditions. Recently, we proposed a Complex-Condition-Controlled Generative Adversarial Network C3-GAN, which tackles both of the challenges and solves the urban traffic estimation problem under various complex conditions by adding a fixed embedding network and an inference network on top of the standard conditional GAN model. The randomly chosen embedding network transforms the complex conditions to latent vectors, and the inference network enhances the connections between the embedded vectors and the traffic data. However, a randomly chosen embedding network cannot always successfully extract features of complex urban conditions, which indicates C3-GAN is unable to uniquely map different urban conditions to proper latent distributions. Thus, C3-GAN would fail in certain traffic estimation tasks. Besides, C3-GAN is hard to train due to vanishing gradients and mode collapse problems. To address these issues, in this article, we extend our prior work by introducing a new deep generative model, namely, C3-GAN+, which significantly improves the estimation performance and model stability. C3-GAN+ has new objective, architecture, and training algorithm. The new objective applies Wasserstein loss to the conditional generation case to encourage stable training. Shared convolutional layers between the discriminator and the inference network help to capture spatial dependencies of traffic more efficiently, part of the shared convolutional layers are used to update the embedding network periodically aiming to encourage good representation and avoid model divergence. Extensive experiments on real-world datasets demonstrate that our C3-GAN+ produces high-quality traffic estimations and outperforms state-of-the-art baseline methods. 

https://proceedings.neurips.cc/paper_files/paper/2024/hash/06477eb61ea6b85c6608d42a222462df-Abstract-Datasets_and_Benchmarks_Track.html 

Accurate prediction of water flow is of utmost importance, particularly for ensuring water supply and informing early actions for floods and droughts. Existing flow prediction methods rely on the input of weather drivers, which hinders their applicability to monitoring small headwater streams due to the limited spatial resolution of existing weather datasets. This paper introduces a new dataset with frequent imagery on streams for water monitoring tasks. Our objective is to automatically predict streamflow for each stream site using frequent images taken at a sub-hourly scale. To overcome the challenge of limited labels for certain stream sites, we employ knowledge transfer from well-observed sites to poorly-observed sites via domain adaptation. As each stream site involves highly variable time series data over long periods, we introduce a novel method STCGAN (Spatial-Temporal Cycle Generative Adversarial Network), which incorporates temporal context by conditioning on the sequence's time and learns overall trends of stream flow variation. It integrates the predictive modeling of streamflow with the cyclic generative process and enhances the prediction with data augmentation using generated synthetic samples. Our experiments demonstrate superior performance of the proposed method using data collected from the West Brook area located in western Massachusetts, US. The proposed method can be further extended to selectively combine information from multiple well-observed stream sites, leading to improved overall performance.