Intelligent highway traffic flow prediction method based on continuous monitor

Abstract

The invention discloses an intelligent high-speed traffic flow prediction method based on a continuous monitor, and belongs to the technical field of traffic flow prediction. An expressway portal frame collects real-time traffic flow data, a feature project is established for historical flow data to obtain multi-dimensional features, feature selection is performed through linear and nonlinear methods, a TCN + GBDT-based network model is established, and the real-time traffic flow data is obtained. Historical big data is used for training, and the model can predict the traffic flow at the next time interval. Compared with a traditional model driving method, a pure sequence feature-based neural network method, a single RNN-type neural network-based method and the like, the method has the advantage that the prediction precision is improved.

Description

technical field [0001] The invention belongs to the technical field of traffic flow forecasting, and more specifically relates to an intelligent high-speed traffic flow forecasting method based on a continuous monitor. Background technique [0002] With the improvement of traffic intelligence level and the rapid development of big data and artificial intelligence technology, timely and effective acquisition of real-time traffic flow data has become a reality. Massive historical data provides a solid data foundation for traffic flow forecasting. In order to give full play to The role of intelligent transportation equipment and the maximum use of massive historical data to better serve traffic management and control, many experts and scholars have carried out a lot of research in the field of traffic flow forecasting. [0003] In previous studies, there are two main modes of traffic flow forecasting models: model-driven and data-driven. [0004] Model-driven is also called pa...

AI Technical Summary

Problems solved by technology

Therefore, the network model with a cyclic structure cannot perform large-scale parallel computing like the CNN architecture, and is not suitable for scenarios that require efficiency;

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