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- W3046142892 abstract "Nowadays, traffic flow prediction, as a vital part of the Intelligent Transportation System (ITS), has attracted considerable attention from both academia and industry. Many prediction methods have been proposed and can be categorized into parametric methods and non-parametric methods. Nonparametric methods, especially Machine Learning (ML)-based methods, compared to parametric methods, need less prior knowledge about the relationship among different traffic patterns and can better fit the non-linear features of traffic data. However, we notice that, due to the complex structure, ML-models require a higher cost of implementation regarding time consumption of training and predicting. Therefore, in this paper, we evaluate not only the accuracy but also the efficiency and scalability of some state-of-the-art ML-models, which is the key to apply a prediction model into the real world. Furthermore, we design an off-line optimization method, Desensitization, to improve the scalability of a given model." @default.
- W3046142892 created "2020-08-03" @default.
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- W3046142892 date "2020-06-01" @default.
- W3046142892 modified "2023-09-23" @default.
- W3046142892 title "The Scalability Analysis of Machine Learning Based Models in Road Traffic Flow Prediction" @default.
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- W3046142892 doi "https://doi.org/10.1109/icc40277.2020.9148964" @default.
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