Publication Type

Journal Article

Version

acceptedVersion

Publication Date

4-2022

Abstract

In this modern era, traffic congestion has become a major source of negative economic and environmental impact for urban areas worldwide. One of the most efficient ways to mitigate traffic congestion is through future traffic prediction. The field of traffic prediction has evolved greatly ever since its inception in the late 70s. Earlier studies mainly use classical statistical models such as ARIMA and its variants. Then, researchers started to focus on machine learning models due to their power and flexibility. As theoretical and technological advances emerge, we enter the era of deep neural network, which gained popularity due to its sheer prediction power which can be attributed to the complex and deep structure. Despite the popularity of deep neural network models in the field of traffic prediction, literature surveys of them are rare. In this work, we present an up-to-date survey of deep neural network for traffic prediction. We will provide a detailed explanation of popular deep neural network architectures commonly used in the traffic flow prediction literatures, categorize and describe the literatures themselves, present an overview of the commonalities and differences between the different work, and finally provide a discussion regarding the challenges and future directions for this field.

Keywords

Deep Neural Network, Deep Learning, Traffic Flow Prediction, Traffic Prediction, Road Network

Discipline

Databases and Information Systems | Numerical Analysis and Scientific Computing | Transportation

Research Areas

Data Science and Engineering

Publication

IEEE Transactions on Knowledge and Data Engineering

Volume

34

Issue

4

First Page

1544

Last Page

1561

ISSN

1041-4347

Identifier

10.1109/TKDE.2020.3001195

Publisher

IEEE

Embargo Period

6-11-2021

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1109/TKDE.2020.3001195

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