Large-scale commuting flow prediction is an essential task to estimate the commuting origin-destination (OD) demand within within a prefecture or the whole nation using multiple auxiliary data. Considering ranked structures of metropolitan areas and increased number of geographical units that need to be maintained, we develop a heterogeneous graph-based model to generate meaningful region embeddings at multiple spatial resolutions for predicting different types of inter-level OD flows. To demonstrate the effectiveness of the proposed method, extensive experiments were conducted using real-world aggregated mobile phone datasets collected from Shizuoka Prefecture, Japan. The results indicate that our proposed model outperforms existing models in terms of a uniform urban structure. We extend the understanding of predicted results using reasonable explanations to enhance the credibility of the model.

Origin–destination (OD) flow d ata a re c ritical for urban planning and traffic system design. Such data are suitable for describing movement at the macroscopic level. However, collecting them on a large scale, such as in a city, is challenging. Their form incompatibility makes using them for other tasks difficult. Therefore, we propose a deep model to learn meaningful OD information on grids within a city to address these problems. We collected multimodal characteristics of regions, such as road network densities and facility distributions, from several open-source datasets and used them as grid signals. We then constructed a spatial attention-based deep graph network to generate grid embeddings and used them to predict the OD volumes. The proposed method was evaluated against a set of baseline approaches using a real-world dataset in Japan. The analysis indicated that our model can extract more accurate latent topographical information from OD graphs and produce reasonable grid embeddings; these representations apply to other downstream tasks.

Human mobility datasets collected from various sources are indispensable for analyzing, predicting, and solving emerging urbanization and population issues. However, such datasets are only available to the public after aggregation and anonymous processing. In recent years, agent-based modeling approaches have addressed this problem by reproducing synthetic human mobility data through simulation. However, the development of such agent models typically requires a large amount of personal location histories as training data for parameter learning, leading to cost and privacy concerns. To overcome this disadvantage, we attempted to explore optimal parameters using a particle filter to alleviate the strict requirement of the data. We tested our method in a local city in Japan using aggregated real-time observation data collected from mobile phone service companies. The results show that the proposed model can achieve satisfactory accuracy using low-resolution data and can therefore be easily used by local governments for municipal applications.