Neural-network-based models for short-term traffic flow forecasting using a hybrid exponential smoothing and Levenberg–Marquardt algorithm
This paper proposes a novel neural network (NN) training method that employs the hybrid
exponential smoothing method and the Levenberg-Marquardt (LM) algorithm, which aims to
improve the generalization capabilities of previously used methods for training NNs for short-
term traffic flow forecasting. The approach uses exponential smoothing to preprocess traffic
flow data by removing the lumpiness from collected traffic flow data, before employing a
variant of the LM algorithm to train the NN weights of an NN model. This approach aids NN …
exponential smoothing method and the Levenberg-Marquardt (LM) algorithm, which aims to
improve the generalization capabilities of previously used methods for training NNs for short-
term traffic flow forecasting. The approach uses exponential smoothing to preprocess traffic
flow data by removing the lumpiness from collected traffic flow data, before employing a
variant of the LM algorithm to train the NN weights of an NN model. This approach aids NN …
Neural-network-based models for short-term traffic flow forecasting using a hybrid exponential smoothing and Levenberg–Marquardt algorithm
This paper proposes a novel neural network (NN) training method that employs the hybrid
exponential smoothing method and the Levenberg-Marquardt (LM) algorithm, which aims to
improve the generalization capabilities of previously used methods for training NNs for short-
term traffic flow forecasting. The approach uses exponential smoothing to preprocess traffic
flow data by removing the lumpiness from collected traffic flow data, before employing a
variant of the LM algorithm to train the NN weights of an NN model. This approach aids NN …
exponential smoothing method and the Levenberg-Marquardt (LM) algorithm, which aims to
improve the generalization capabilities of previously used methods for training NNs for short-
term traffic flow forecasting. The approach uses exponential smoothing to preprocess traffic
flow data by removing the lumpiness from collected traffic flow data, before employing a
variant of the LM algorithm to train the NN weights of an NN model. This approach aids NN …
