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Hace 4 días · Traffic flow prediction is a crucial component of urban traffic management and planning, with its accuracy being paramount for optimizing traffic flow, reducing congestion, improving road throughput efficiency, and enhancing urban living standards.
11 de may. de 2024 · —Accurate and timely traffic flow prediction can effectively improve road network efficiency and alleviate a series of negative impacts caused by traffic congestion. Recent research has focused on exploring the spatiotemporal correlations of traffic data, and achieved some progress.
10 de may. de 2024 · This paper introduces a novel deep learning framework for traffic flow prediction. Firstly, we propose an AGCRN that integrates the transformer algorithm to effectively capture long-range temporal correlations.
18 de may. de 2024 · In this paper, we proposed a novel model named 3D spatial–temporal-based adaptive modeling graph convolutional network (3D(STAMGCN)) that addresses for traffic flow data in better periodicity modeling. In contrast to earlier studies, 3D(STAMGCN) approaches the task of traffic flow prediction as a periodic residual learning problem.
27 de abr. de 2024 · Abstract. As a critical component of Intelligent Transportation Systems (ITS), traffic flow prediction is indispensable for vehicle routing and transportation management. However, traffic prediction is an intractable task due to the complex spatial–temporal dependencies.
14 de may. de 2024 · The results demonstrate that our model outperforms existing state-of-the-art GCN-based models and traditional baseline methods. The accurate prediction of traffic conditions is essential for effective and efficient traffic management and control. The dynamic and complex nature of traffic.
14 de may. de 2024 · Guo et al. denoted the traffic flow data as a 3D tensor and then proposed a deep spatial-temporal 3D convolutional neural called ST-3DNet to predict traffic congestion . However, these methods assume a regular distribution of sensors/stations within the traffic network, but in real life, their distribution is usually irregular in most cases, showing a non-Euclidean structure.
