The ability to accurately predict urban traffic flows is crucial for optimising city ***,various methods for forecasting urban traffic have been developed,focusing on analysing historical data to understand complex mo...
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The ability to accurately predict urban traffic flows is crucial for optimising city ***,various methods for forecasting urban traffic have been developed,focusing on analysing historical data to understand complex mobility *** learning techniques,such as graph neural networks(GNNs),are popular for their ability to capture spatio-temporal ***,these models often become overly complex due to the large number of hyper-parameters *** this study,we introduce dynamic multi-graph Spatial-Temporal graph Neural Ordinary Differential Equation Networks(DMST-GNODE),a framework based on ordinary differential equations(ODEs)that autonomously discovers effective spatial-temporal graph neural network(STGNN)architectures for traffic prediction *** comparative analysis of DMST-GNODE and baseline models indicates that DMST-GNODE model demonstrates superior performance across multiple datasets,consistently achieving the lowest Root Mean Square Error(RMSE)and Mean Absolute Error(MAE)values,alongside the highest *** the BKK(Bangkok)dataset,it outperformed other models with an RMSE of 3.3165 and an accuracy of 0.9367 for a 20-min interval,maintaining this trend across 40 and 60 ***,on the PeMS08 dataset,DMST-GNODE achieved the best performance with an RMSE of 19.4863 and an accuracy of 0.9377 at 20 min,demonstrating its effectiveness over longer *** Los_Loop dataset results further emphasise this model’s advantage,with an RMSE of 3.3422 and an accuracy of 0.7643 at 20 min,consistently maintaining superiority across all time *** numerical highlights indicate that DMST-GNODE not only outperforms baseline models but also achieves higher accuracy and lower errors across different time intervals and datasets.
Travel time estimation (TTE) is a crucial and challenging task due to the complex spatial and dynamic temporal correlations between local and global traffic regions. Though many existing methods have used multi-graph ...
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Travel time estimation (TTE) is a crucial and challenging task due to the complex spatial and dynamic temporal correlations between local and global traffic regions. Though many existing methods have used multi-graph structures to model traffic variations, the majority of them are unable to capture such sophisticated local and global perspectives dynamically at different time-intervals. Furthermore, these methods have used shallow graphs (i.e., based on speed or distance between traffic nodes), thereby limiting their ability to learn the latent dependencies. To overcome these limitations, we propose a novel Hybrid Local -Global Spatio-Temporal (HLGST) framework for TTE. Specifically, we first introduce a dynamic composition unit that builds local traffic information and multi-dynamic semantic graphs based on the similarities between nodes. Then, the HLGST model learns the local and global dependencies via hybrid correlation method. It mainly comprises two modules: (1) local correlation module that integrates casual TCN layers with a self-attention mechanism to capture local dependencies of traffic patterns;(2) triplet-siamese GCN (TS-GCN) module that is employed to smoothly capture global dependencies based on triplet relationships between multi-dynamical semantic graphs. Moreover, we build a dynamic adaptive learning algorithm to transfer the gained knowledge and leverage it to model multi-objective prediction tasks collaboratively. Extensive experiments conducted on two real-world traffic datasets (Xi'an and Chengdu) demonstrate that our HLGST model outperforms compared baselines and achieves significant improvement.
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