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Hace 2 días · By aggregating predictions from multiple perspectives, this approach could better handle complex traffic scenarios where predictions from a single autonomous vehicle might be limited. Furthermore, this method could enhance robustness against individual prediction errors, thereby improving the overall reliability of the system.
Hace 5 días · Real-time and accurate traffic flow forecasting plays a crucial role in transportation systems and holds great significance for urban traffic planning, traffic management, traffic control, and more. The most difficult challenge is the extraction of temporal features and spatial correlations of nodes in traffic flow forecasting. Meanwhile, graph convolutional networks has shown good performance ...
12 de jun. de 2024 · Abstract: Traffic flow prediction is essential for alleviating congestion and enhancing traffic management. While previous efforts have mainly concentrated on road-level predictions, the advancement of technology and the growing presence of automated vehicles have encouraged lane-level forecasting for more efficient driving and routing decisions.
29 de jun. de 2024 · This is obviously helpful for better prediction, so capturing the temporal dependence of traffic variations is also essential. (3) External influences, such as weather and major events within the city, also affect pedestrian and traffic flow predictions. This further emphasizes the importance of short-term traffic flow prediction.
27 de jun. de 2024 · Urban traffic speed prediction aims to estimate the future traffic speed for improving urban transportation services. Enormous efforts have been made to exploit Graph Neural Networks (GNNs) for modeling spatial correlations and temporal dependencies of traffic speed evolving patterns, regularized by graph topology.While achieving promising results, current traffic speed prediction methods ...
1 de jul. de 2024 · The traffic prediction is one of thekey requirement for establishing the ITS. In this paper we present our study on ARIMA model with optimized hyper-parameter using grid search technique for traffic flow predictions. The model validation is done on the whole day traffic flow, morning and evening peak time traffic flow datasets.
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