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Conference Paper Urban Traffic Prediction using Congestion Diffusion Model
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Authors
Sung-Soo Kim, Moonyoung Chung, Young-Kuk Kim
Issue Date
2020-11
Citation
International Conference on Consumer Electronics (ICCE) 2020 : Asia, pp.360-363
Publisher
IEEE
Language
English
Type
Conference Paper
DOI
https://dx.doi.org/10.1109/ICCE-Asia49877.2020.9276823
Abstract
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.
KSP Keywords
Diffusion Model, Distance metric, Experimental Result, Graph signals, Modeling techniques, Prediction methods, Spatial dependency, Traffic congestion, Urban traffic networks, adjacency matrix, deep learning(DL)