Learning to Generate Temporal Origin-destination Flow Based-on Urban Regional Features and Traffic Information

Abstract

Origin-destination (OD) flow contains population mobility information between every two regions in the city, which is of great value in urban planning and transportation management. Nevertheless, the collection of OD flow data is extremely difficult due to the hindrance of privacy issues and collection costs. Significant efforts have been made to generate OD flow based on urban regional features, e.g., demographics, land use, and so on, since spatial heterogeneity of urban function is the primary cause that drives people to move from one place to another. On the other hand, people travel through various routes between OD, which will have effects on urban traffic, e.g., road travel speed and time. These effects of OD flows reveal the fine-grained spatiotemporal patterns of population mobility. Few works have explored the effectiveness of incorporating urban traffic information into OD generation. To bridge this gap, we propose to generate real-world daily temporal OD flows enhanced by urban traffic information in this paper. Our model consists of two modules: Urban2OD and OD2Traffic . In the Urban2OD module, we devise a spatiotemporal graph neural network to model the complex dependencies between daily temporal OD flows and regional features. In the OD2Traffic module, we introduce an attention-based neural network to predict urban traffic based on OD flow from the Urban2OD module. Then, by utilizing gradient backpropagation, these two modules are able to enhance each other to generate high-quality OD flow data. Extensive experiments conducted on real-world datasets demonstrate the superiority of our proposed model over the state of the art.