Knowledge-Based Multiple Relations Modeling for Traffic Forecasting

Traffic forecasting is a critical task in intelligent transportation systems. In recent years, lots of methods have been proposed and achieved significant progress in modeling highly nonlinear and complex spatiotemporal pattern for traffic forecasting. However, most methods neglect the specific internal and external factors of the traffic system, such as the road connections, buildings surrounding each place, transfer stations, etc. The main challenges of utilizing the diverse knowledge from internal and external factors are to represent and fuse the impact of various factors. Few works use distance-based adjacency matrices to represent factors and the element-wise multiplication to fuse them, which may lead to even worse performances. In this paper, we propose to utilize knowledge graph to represent traffic system factors and design a novel neural network to exploit them for traffic foresting. First, we model the relations between factors and traffic conditions from the perspective of the knowledge graph and express them as unified triplets. Then, we generate multi-hop path features with embeddings learned from the knowledge representation model and multi-hop paths searched from the graph structure. Next, we present a knowledge-based multi-hop network (KMHNet) that uses an attention-based module to learn the correlation from multi-hop path features. Finally, to evaluate the performance of the proposed method, we build two real-world datasets both containing a traffic condition sub-dataset and a traffic knowledge graph. Experiments on two datasets demonstrate that our proposed KMHNet outperforms eight well-known methods. The code is publicly available at https://github.com/2448845600/KMHNet.