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학술대회 Urban Traffic Prediction using Congestion Diffusion Model
Cited 4 time in scopus Download 2 time Share share facebook twitter linkedin kakaostory
김성수, 정문영, 김영국
International Conference on Consumer Electronics (ICCE) 2020 : Asia, pp.360-363
20HS5800, 클라우드 엣지 기반 도시교통 브레인 핵심기술 개발, 정문영
Traffic prediction is an essential task in reducing traffic congestions and improving transportation. However, this task is challenging due to the complex spatio-temporal dynamics of urban traffic networks which are difficult to model. Previous approaches principally concentrate on modeling the Euclidean correlations among spatially adjacent sensors in a road network. In this paper, we propose a new weight modeling technique for the adjacency matrix using the path distance metric for the graph signals to provide accurate spatial properties according to the connection information of the urban road network. We exploit a diffusion-based traffic prediction method for modeling spatial dependency and capturing the temporal dynamics. The experimental result shows that the recent deep learning techniques with the proposed spatial model are promising solutions to the traffic prediction.
graph embedding, graph neural network, spatiotemporal dynamics, traffic forecasting
KSP 제안 키워드
Diffusion Model, Distance metric, Experimental Result, Graph Embedding, Graph signals, Modeling techniques, Neural networks, Prediction methods, Spatial dependency, Spatiotemporal dynamics, Traffic congestion