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On Training Traffic Predictors via Broad Learning Structures: A Benchmark Study
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DOI：10.1109/TSMC.2020.3006124.png)
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摘要
En 中文
A fast architecture for real-time (i.e., minute-based) training of a traffic predictor is studied, based on the so-called broad learning system (BLS) paradigm. The study uses various traffic datasets by the California Department of Transportation, and employs a variety of standard algorithms (LASSO regression, shallow and deep neural networks, stacked autoencoders, convolutional, and recurrent neural networks) for comparison purposes: all algorithms are implemented in MATLAB on the same computing platform. The study demonstrates a BLS training process two-three orders of magnitude faster (tens of seconds against tens-hundreds of thousands of seconds), allowing unprecedented real-time capabilities. Additional comparisons with the extreme learning machine architecture, a learning algorithm sharing some features with BLS, confirm the fast training of least-square training as compared to gradient training.
Keyword:
Training
Prediction algorithms
Computer architecture
Zinc
Testing
Real-time systems
Learning systems
Broad learning system (BLS)
least-square methods
real-time software
real-time training
traffic flow prediction
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期刊
IF:
10.5
论文数:
1.1W
被引数:
5.0W
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