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A deep learning urban traffic congestion forecast model blending the temporal continuity and periodicity

Published: 14 October 2022 Publication History

Abstract

Traffic congestion has become an inevitable and difficult disease in the process of urban development, and it has also brought harm and hidden dangers to citizens' travel and urban development. The emergence of GCN solves the problem of capturing the spatial characteristics of urban road traffic. Based on this, we propose a new method that considers the periodicity of traffic patterns and builds a neural network model with multiple time scales to capture more detailed features. And the experiment proves that our model is better in predicting traffic congestion.

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cover image ACM Other conferences
ICCIR '22: Proceedings of the 2022 2nd International Conference on Control and Intelligent Robotics
June 2022
905 pages
ISBN:9781450397179
DOI:10.1145/3548608
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 14 October 2022

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