Data-driven approach for instantaneous vehicle emission predicting using integrated deep neural network

Abdul Motin Howlader; Dilip Patel Robert Gammariello

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Data-driven approach for instantaneous vehicle emission predicting using integrated deep neural network

机译：Data-driven approach for instantaneous vehicle emission predicting using integrated deep neural network

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摘要

This paper details how instantaneous vehicle emissions, namely, CO_2, CO, NO_X, and HC from light-duty vehicles, can be predicted using the integrated deep neural network method (NNM). The deep-learning algorithms, i.e., short-term memory (LSTM), gated recurrent unit (GRU), and recurrent neural network (RNN) methods, were applied to predict the emissions. Finally, an integrated method using LSTM, RNN, and GRU was used to determine if the integrated method was better at increasing the prediction performance of vehicle emissions. Each model performance was evaluated by calculating the mean squared error (MSE), root mean squared error (RMSE), and normalize root mean squared error (nRMSE) values. The results indicate that the integrated LSTM NNM provided the best overall emission prediction compared to the other methods. This integrated model can help to develop new policies and regulations for vehicle emissions.

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来源
《Transportation research, Part D. Transport and environment》 |2023年第3期|103654.1-103654.18|共18页
作者
Abdul Motin Howlader; Dilip Patel Robert Gammariello;
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作者单位

Mobile Source Laboratory Division, California Air Resources Board, 4001 Iowa Ave, Riverside, 92507, CA, USA;

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正文语种英语
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Vehicle emission prediction; Deep neural network; Long short-term memory (LSTM); Gated recurrent unit (GRU); Recurrent neural network (RNN); Integrated deep neural network;

Data-driven approach for instantaneous vehicle emission predicting using integrated deep neural network

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