Layerwise Recurrent Autoencoder for Real-World Traffic Flow Forecasting

Zhao, Junhui; Zhu, Tianqi; Zhao, Renda; Zhao, Peize

doi:10.1007/978-3-030-36204-1_6

Cited by 4 publications

(1 citation statement)

References 12 publications

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“…However, this method is very complex and time-consuming for converting spatiotemporal traffic dynamics to images. The Stacked Autoencoder (SAE) model for traffic flow prediction was implemented in [21][22][23] to enhance feature extraction. On the other hand, one of the main disadvantages of SAEs is that they do not produce significant effect when errors are present in the first layers [24].…”

Section: Software Implementation Of Online Neural Networkmentioning

confidence: 99%

An Efficient Hardware Design for a Low-Latency Traffic Flow Prediction System Using an Online Neural Network

2021

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Neural networks are computing systems inspired by the biological neural networks in human brains. They are trained in a batch learning mode; hence, the whole training data should be ready before the training task. However, this is not applicable for many real-time applications where data arrive sequentially such as online topic-detection in social communities, traffic flow prediction, etc. In this paper, an efficient hardware implementation of a low-latency online neural network system is proposed for a traffic flow prediction application. The proposed model is implemented with different Machine Learning (ML) algorithms to predict the traffic flow with high accuracy where the Hedge Backpropagation (HBP) model achieves the least mean absolute error (MAE) of 0.001. The proposed system is implemented using floating point and fixed point arithmetics on Field Programmable Gate Array (FPGA) part of the ZedBoard. The implementation is provided using BRAM architecture and distributed memory in FPGA in order to achieve the best trade-off between latency, the consumption of area, and power. Using the fixed point approach, the prediction times using the distributed memory and BRAM architectures are 150 ns and 420 ns, respectively. The area delay product (ADP) of the proposed system is reduced by 17 × compared with the hardware implementation of the latest proposed system in the literature. The execution time of the proposed hardware system is improved by 200 × compared with the software implemented on a dual core Intel i7-7500U CPU at 2.9 GHz. Consequently, the proposed hardware model is faster than the software model and more suitable for time-critical online machine learning models.

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Section: Software Implementation Of Online Neural Networkmentioning

confidence: 99%

An Efficient Hardware Design for a Low-Latency Traffic Flow Prediction System Using an Online Neural Network

2021

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Research on Traffic Flow Forecasting Based on Deep Learning

Zhang,

Zhao,

Cao

et al. 2023

Communications in Computer and Information Science

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Deep learning model for two-fluid flows

et al. 2023

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Various industries rely on numerical tools to simulate multiphase flows due to the wide occurrence of this phenomenon in nature, manufacturing processes, or the human body. However, the significant computation burden required for such simulations directs the research interest toward incorporating data-based approaches in the solution loop. Although, these approaches returned significant results in various domains, incorporating them in the computational fluid dynamics field is wrangled by their casting aside of the already known governing constitutional laws along with the natural incompatibility of various models with unstructured irregular discretization spaces. This work suggests a coupling framework, between a traditional finite element CFD solver and a deep learning model, for tackling multiphase fluid flows without migrating the benefits of physics-enriched traditional solvers. The tailored model architecture, along with the coupling framework, allows tackling the required problem with a dynamically adapted unstructured irregular triangular mesh, thus dodging the limitation of traditional convolution neural networks. Moreover, the various ingredients that allowed the model to simulate the complex and computation-demanding Navier-Stokes flow equation, such as relying on a sequential validation dataset while exposing the model training to a noise inherited from the quality of its inferring, along with the proper choice of model inputs, are highlighted and elaborated throughout this paper. To the authors' knowledge, this work is the first of its type to introduce a data-based graph-based approach for solving multiphase flow problems with a level-set interface capturing method.

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Layerwise Recurrent Autoencoder for Real-World Traffic Flow Forecasting

Cited by 4 publications

References 12 publications

An Efficient Hardware Design for a Low-Latency Traffic Flow Prediction System Using an Online Neural Network

An Efficient Hardware Design for a Low-Latency Traffic Flow Prediction System Using an Online Neural Network

Research on Traffic Flow Forecasting Based on Deep Learning

Deep learning model for two-fluid flows

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