Traffic Origin-Destination Demand Prediction via Multichannel Hypergraph Convolutional Networks

Accurate prediction of origin-destination (OD) demand is critical for service providers to efficiently allocate limited resources in regions with high travel demands. However, OD distributions pose significant challenges, characterized by high sparsity, complex spatial correlations within regions or chains, and potential repetition due to the recurrence of similar semantic contexts. These challenges impede traditional graph-based approaches, which connect two vertices through an edge, from performing effectively in OD prediction. Thus, we present a novel multichannel hypergraph convolutional neural network (MC-HGCN) to overcome the above challenges. The model innovatively extracts distinctive features from the channels of inflows, outflows, and OD flows, to conquer the high sparsity in OD matrices. High-order spatial proximity within regions and OD chains are then modeled by the three adjacency hypergraphs constructed for the above three channels. In each adjacency hypergraph, multiple neighboring stations are treated as vertices, while multiple OD pairs constitute hyperedges. These structures are learned by hypergraph convolutional networks for latent spatial correlations. On this basis, a semantic hypergraph is created for the OD channel to model OD distributions lacking spatial proximity but sharing semantic correlations. It utilizes hyperedges to represent semantic correlations among OD pairs whose origins and destinations both possess similar point-of-interest (POI) functions, before learned by a hypergraph convolutional network (HGCN). Both spatial and semantic correlations intrinsic to OD flows are accordingly captured and embedded into a gated recurrent unit (GRU) to unveil hidden spatiotemporal dependencies among OD distributions. These embedded correlations are ultimately integrated through a multichannel fusion module to enhance the prediction of OD flows, even for minor ones. Our model is validated through experiments on three public datasets, demonstrating its robust performances across long and short time steps. Findings may contribute theoretical insights for practical applications, such as coordinating traffic scheduling or route planning.