Zobrazeno 1 - 10
of 13 556
pro vyhledávání: '"Traffic forecasting"'
Due to the global trend towards urbanization, people increasingly move to and live in cities that then continue to grow. Traffic forecasting plays an important role in the intelligent transportation systems of cities as well as in spatio-temporal dat
Externí odkaz:
http://arxiv.org/abs/2410.19192
Traffic forecasting in Intelligent Transportation Systems (ITS) is vital for intelligent traffic prediction. Yet, ITS often relies on data from traffic sensors or vehicle devices, where certain cities might not have all those smart devices or enablin
Externí odkaz:
http://arxiv.org/abs/2410.15589
Mobile traffic forecasting allows operators to anticipate network dynamics and performance in advance, offering substantial potential for enhancing service quality and improving user experience. However, existing models are often task-oriented and ar
Externí odkaz:
http://arxiv.org/abs/2410.15322
Recently, spatial-temporal forecasting technology has been rapidly developed due to the increasing demand for traffic management and travel planning. However, existing traffic forecasting models still face the following limitations. On one hand, most
Externí odkaz:
http://arxiv.org/abs/2410.09356
Traffic forecasting is a cornerstone of smart city management, enabling efficient resource allocation and transportation planning. Deep learning, with its ability to capture complex nonlinear patterns in spatiotemporal (ST) data, has emerged as a pow
Externí odkaz:
http://arxiv.org/abs/2410.00385
Spatiotemporal Graph Neural Networks (ST-GNNs) and Transformers have shown significant promise in traffic forecasting by effectively modeling temporal and spatial correlations. However, rapid urbanization in recent years has led to dynamic shifts in
Externí odkaz:
http://arxiv.org/abs/2411.11448
Traffic forecasting plays a key role in Intelligent Transportation Systems, and significant strides have been made in this field. However, most existing methods can only predict up to four hours in the future, which doesn't quite meet real-world dema
Externí odkaz:
http://arxiv.org/abs/2411.00844
We study the analysis of all the movements of the population on the basis of their mobility from one node to another, to observe, measure, and predict the impact of traffic according to this mobility. The frequency of congestion on roads directly or
Externí odkaz:
http://arxiv.org/abs/2411.08052
Recent advancements in Spatiotemporal Graph Neural Networks (ST-GNNs) and Transformers have demonstrated promising potential for traffic forecasting by effectively capturing both temporal and spatial correlations. The generalization ability of spatio
Externí odkaz:
http://arxiv.org/abs/2410.00373
Traffic forecasting has emerged as a crucial research area in the development of smart cities. Although various neural networks with intricate architectures have been developed to address this problem, they still face two key challenges: i) Recent ad
Externí odkaz:
http://arxiv.org/abs/2408.10822