A GAN framework-based dynamic multi-graph convolutional network for origin–destination-based ride-hailing demand prediction

Ride-hailing demand prediction plays an important role in ride-hailing vehicle scheduling, traffic condition control and intelligent transportation system construction. Accurate and real-time ride-hailing demand prediction is crucial for improving supply–demand imbalance, vehicle utilization and traffic conditions. However, most existing works mainly address the region-based demand prediction whereas only a few works focus on the origin–destination (OD)-based demand prediction. To address the issue, we develop several dynamic OD graphs to character the ride-hailing demand transactions between the origin and destination. We propose a novel deep learning model, referred to as the Dynamic Multi-Graph Convolutional Network with Generative Adversarial Network structure (DMGC-GAN), to investigate the challenging problem. Different from previous studies, we develop the temporal multi-graph convolutional network (TMGCN) layer with different dynamic OD graphs to capture the spatial topologies contained in the dynamic OD graphs in terms of time, and exploit GAN structure to overcome the high sparsity of OD demand. We conduct extensive numerical experiments on the real-world ride-hailing demand dataset (from Manhattan district, New York City). The results demonstrate that the model we propose performs the best against to nine baseline models.