Dynamic Graph Learning Based on Hierarchical Memory for Origin-Destination Demand Prediction
This work addresses a specific bottleneck in traffic forecasting for urban planning and transportation systems, offering an incremental improvement over existing spatiotemporal methods.
The paper tackles the challenge of predicting origin-destination (OD) demand in traffic forecasting, which is difficult due to the quadratic scaling of OD pairs, by proposing a dynamic graph learning framework with a hierarchical memory updater and spatiotemporal propagation, achieving superior results on two real-world datasets.
Recent years have witnessed a rapid growth of applying deep spatiotemporal methods in traffic forecasting. However, the prediction of origin-destination (OD) demands is still a challenging problem since the number of OD pairs is usually quadratic to the number of stations. In this case, most of the existing spatiotemporal methods fail to handle spatial relations on such a large scale. To address this problem, this paper provides a dynamic graph representation learning framework for OD demands prediction. In particular, a hierarchical memory updater is first proposed to maintain a time-aware representation for each node, and the representations are updated according to the most recently observed OD trips in continuous-time and multiple discrete-time ways. Second, a spatiotemporal propagation mechanism is provided to aggregate representations of neighbor nodes along a random spatiotemporal route which treats origin and destination as two different semantic entities. Last, an objective function is designed to derive the future OD demands according to the most recent node representations, and also to tackle the data sparsity problem in OD prediction. Extensive experiments have been conducted on two real-world datasets, and the experimental results demonstrate the superiority of the proposed method. The code and data are available at https://github.com/Rising0321/HMOD.