Multi-scale synchronous contextual network for fine-grained urban flow inference

Abstract

In modern urban planning and traffic management, accurately inferring fine-grained urban flows is crucial. This reduces the need for extensive deployment of sensing devices and yields significant financial savings. However, most existing methods overlook the complexity of flow dependencies in urban areas and the specificity of each external influencing factor when addressing this urban inference challenge. To enhance the accuracy of fine-grained urban flow inference, this paper proposes a novel multi-scale synchronous contextual framework named MS-SCL. This model introduces a multi-scale global modeling module designed to capture comprehensive information about urban traffic dynamics. It integrates linear bottleneck attention and two distinct large kernel convolutions, enabling the synchronous learning of three global representations across different scales. Moreover, MS-SCL employs a multi-view approach to generate individual feature maps for each external influencing factor, rather than merging them together. This strategy provides a more precise differentiation of the impact from each external factor on the traffic flow inference. Extensive experiments conducted on three urban datasets validate that the MS-SCL model outperforms current baseline methods. The source code for the algorithm is available at https://github.com/panlinnn/MS-SCL.