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DTG-LKNet: Dual spatio-temporal graphs and large-kernel convolutions for traffic prediction

algorithms-and-analysis-of-algorithms
artificial-intelligence
data-mining-and-machine-learning
spatial-and-geographic-information-systems
neural-networks

Abstract

Accurate traffic flow prediction is central to Intelligent Transportation Systems yet remains difficult due to non-Euclidean spatial structure, long-range propagation, and time-varying delays. However, existing deep learning methods have key limitations, including reliance on fixed temporal partitioning, small-kernel locality that restricts receptive fields, and graph constructions that lack long-range, dynamic correlations. This work presents DTG-LKNet, a Transformer-based architecture that packages a large-kernel–dominated temporal convolution module with a dual spatio-temporal graph in a unified framework. On the temporal side, Deformable Patch Sampling learns sampling offsets around salient timestamps, and large kernels expand the effective receptive field without deep dilation stacks, while a complementary small-kernel branch preserves local detail. On the spatial side, DTG-LKNet fuses a functional-similarity graph with the physical road-network topology to represent long-range correlations. Comprehensive experiments on three large-scale benchmarks demonstrate consistent state-of-the-art performance against strong baselines. This paper further visualizes the effective receptive field to confirm.
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