A large-scale real-time traffic flow prediction method of the present invention is based on fuzzy logic and deep LSTM, which relates to a technical field of urban intelligent traffic management. The method includes steps of: selecting an urban road network scene to collect color images of real-time traffic flow congestion information; obtaining congestion levels of multiple intersections according to the color images, which are used in a data training set; and forming a data sensing end of FDFP through a fuzzy mechanism; establishing a deep LSTM neural network, performing deep learning on the training data set, and constructing a prediction end of the FDFP; construct a graph of road intersections and formulate a k-nearest neighbors-based discounted averaging for obtaining congestion on the edges; and inputting real-time traffic information received from a server into an FDFP model.
Large-scale real-time traffic flow prediction method based on fuzzy logic and deep LSTM
2021-07-08
Patent
Electronic Resource
English
Large-scale real-time traffic flow prediction method based on fuzzy logic and deep LSTM
European Patent Office | 2023
|Traffic Flow Velocity Prediction Based on Real Data LSTM Model
British Library Conference Proceedings | 2021
|Traffic Flow Velocity Prediction Based on Real Data LSTM Model
SAE Technical Papers | 2021
|European Patent Office | 2021
|