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Smart city article details

Title	Predicting Urban Traffic Flow Based On Deep Meta-Learning
ID_Doc	42773
Authors	Zhu W.; Kong H.; Cai W.; Zhu W.
Year	2024
Published	ACM International Conference Proceeding Series
DOI	http://dx.doi.org/10.1145/3690407.3690601
Abstract	This paper aims to explore the application of deep meta-learning in urban traffic flow prediction, analyzing its advantages over traditional methods and the challenges it faces. Firstly, we introduce the background and importance of urban traffic flow prediction, along with the current application of deep learning technologies in this field. Subsequently, the paper elaborates on the theoretical foundations and key techniques of deep meta-learning, including the design of meta-models, training strategies, and feature integration mechanisms. Additionally, through experimental comparative analysis, we demonstrate the performance of deep meta-models in traffic flow prediction tasks and discuss their potential and limitations in practical applications. Finally, the paper summarizes research findings and proposes future research directions, aiming to provide more efficient and accurate prediction tools for urban traffic management and to promote the development of smart cities. Through in-depth research, we seek to offer new perspectives and solutions to the field of urban traffic flow prediction, contributing to the advancement of intelligent transportation systems. © 2024 Copyright held by the owner/author(s).
Author Keywords	Deep learning; Deep meta-learning; Graph neural networks; Traffic prediction

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