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DDP-GCN: Multi-Graph Convolutional Network for Spatiotemporal Traffic Forecasting
Traffic speed forecasting is one of the core problems in Intelligent
Transportation Systems. For a more accurate prediction, recent studies started
using not only the temporal speed patterns but also the spatial information on
the road network through the graph convolutional networks. Even though the road
network is highly complex due to its non-Euclidean and directional
characteristics, previous approaches mainly focus on modeling the spatial
dependencies only with the distance. In this paper, we identify two essential
spatial dependencies in traffic forecasting in addition to distance, direction
and positional relationship, for designing basic graph elements as the smallest
building blocks. Using the building blocks, we suggest DDP-GCN (Distance,
Direction, and Positional relationship Graph Convolutional Network) to
incorporate the three spatial relationships into prediction network for traffic
forecasting. We evaluate the proposed model with two large-scale real-world
datasets, and find 7.40% average improvement for 1-hour forecasting in highly
complex urban networks
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