Multimodal fusion for large-scale traffic prediction with heterogeneous retentive networks,Information Fusion

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Multimodal fusion for large-scale traffic prediction with heterogeneous retentive networks
Information Fusion ( IF 14.7 ) Pub Date : 2024-09-13 , DOI: 10.1016/j.inffus.2024.102695
Yimo Yan , Songyi Cui , Jiahui Liu , Yaping Zhao , Bodong Zhou , Yong-Hong Kuo

Traffic speed prediction is a critical challenge in transportation research due to the complex spatiotemporal dynamics of urban mobility. This study proposes a novel framework for fusing diverse data modalities to enhance short-term traffic speed forecasting accuracy. We introduce the Heterogeneous Retentive Network (H-RetNet), which integrates multisource urban data into high-dimensional representations encoded with geospatial relationships. By combining the H-RetNet with a Gated Recurrent Unit (GRU), our model captures intricate spatial and temporal correlations. We validate the approach using a real-world Beijing traffic dataset encompassing social media, real estate, and point of interest data. Experiments demonstrate superior performance over existing methods, with the fusion architecture improving robustness. Specifically, we observe a 21.91% reduction in MSE, underscoring the potential of our framework to inform and enhance traffic management strategies.

更新日期：2024-09-13

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