Abstract
In order to improve the rationality and effectiveness of intelligent traffic control and management on urban roads, a bidirectional linear recurrent neural network-based traffic flow prediction method is proposed from the perspective of spatial and temporal characteristics of traffic flow. The method effectively combines the characteristics of fast and accurate bilinear polynomial solution and dynamic calibration of recurrent neural network, and adopts particle swarm algorithm to realize the dynamic pruning process of redundant neurons and weights, which improves the convergence speed and prediction accuracy of the algorithm. The algorithm is trained and experimented with video data, and a comparative analysis is conducted. The results show that the method can achieve accurate prediction of road traffic flow, the traffic flow prediction accuracy reaches more than 90%, meeting the data accuracy requirements of actual traffic management and control, and the convergence speed of the algorithm has also been significantly improved.
Short-Term Traffic Flow Prediction Using the Modified Elman Recurrent Neural Network Optimized Through a Genetic Algorithm