基于深度学习的交通流预测研究进展

Yongjia Lei, Z. Cui, Linyan Dai, Ning Xiao, Xiangjie Wang, Xiangfang Ma, Ying Hong
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引用次数: 0

摘要

众所周知,近年来,深度学习在学术和工业领域都得到了相当广泛的应用。研究人员青睐其强大的便携性和良好的性能。虽然深度学习在交通流预测方面表现出了令人满意的表现,但其在该领域的具体发展过程并不十分清晰,这也不利于研究人员对模型的选择和调整。本文主要回顾了神经网络出现前后在交通预测中应用的一些重要模型,并挖掘了它们出现的原因。通过回顾和挖掘,我们梳理了交通流预测模型的发展历程,为我们后续的研究提供了一些启发。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The Development of Traffic Flow Prediction Based on Deep Learning: A Literature Review
As is well acknowledged, deep learning has been fairly widely used in both academic and industrial fields in recent periods. Researchers favour its strong portability and good performance. Although deep learning has shown satisfactory performance in traffic flow prediction, its specific development process in such a field is not very clear, which is also not conducive for researchers to choose and adjust the model. This paper mainly retraces some significant models applied in traffic prediction before and after the advent of neural networks and digs out the reasons for their appearance. Through review and mining, we have sorted out the development history of traffic flow prediction models, providing us with some inspiration for subsequent research.
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