Parking Space Prediction Algorithm Based on Recurrent Neural Network and Ensemble Learning Algorithm

The essence of parking space prediction is a practical application of time series prediction problem. It provides effective auxiliary information for traffic management and personal travel. As a new demand, they face challenges in data acquisition, accuracy and robustness. The parking space prediction algorithm based on recurrent neural network (RNN) and ensemble learning algorithm is proposed in this paper. However, because the neural network needs large data training, the lack of parking space data in specific areas is a common challenge for researchers. In this paper, a new network model structure based on ensemble learning algorithm is proposed, which is called E-RNN. The ensemble learning algorithm model is used as the primary learner and the neural network as the secondary learner for the ensemble learning. By using the adaptability of the algorithm in ensemble learning to the collective data, the neural network can get better prediction effect under the training of small data sets. Finally, the parameters of the new model are optimized by using particle swarm optimization (PSO) algorithm, and a better model is obtained. The experimental results show that the algorithm has high accuracy and robustness for parking space prediction with a small data sets.