基于深度学习的短时交通流预测模型

交通流预测在智能交通领域有着重要的现实意义。由于交通流数据受多种因素影响,平稳性差、随机性强，呈现出高度非线性的特征，使得交通流预测极为困难。针对短时交通流预测准确性的要求，本文提出一种基于互补集成经验模态分解(Complete Ensemble Empirical Mode Decomposition, CEEMD)，并结合卷积神经网络(Convolutional Neural Networks, CNN)和长短期记忆网络(Long Short-Term Memory, LSTM)的短时交通流预测方法。模型通过CEEMD信号分解减少噪声对交通流数据预测的影响，采用CNN、LSTM充分挖掘数据的时空特征，使得模型做出更加准确的判断，从而提高神经网络的学习效率。在真实交通流数据上进行实验验证，结果表明，本文提出的模型可以有效提高交通流预测的准确性。

Short-term Traffic Flow Prediction Model Based on Deep Learning

Traffic flow prediction has important and practical significance in the field of intelligent transportation. Because traffic flow data is affected by many factors, leads to poor stability, strong randomness, and presents a highly non-linear characteristic, it is extremely difficult to predict traffic flow. Aiming at the requirements of the accuracy of short-term traffic flow prediction, this paper proposes a short-term traffic flow prediction method based on CEEMD(Complete Ensemble Empirical Mode Decomposition, CEEMD), combined with CNN(Convolutional Neural Networks, CNN) and LSTM(Long Short-Term Memory, LSTM). The model uses CEEMD signal decomposition to reduce the impact of noise on traffic flow data prediction, CNN and LSTM are used to fully mine the temporal and spatial characteristics of the data, so that the model can make more accurate judgments and improve the learning efficiency of the neural network. Experimental verification on real traffic flow data shows that the model proposed in this paper can effectively improve the accuracy of traffic flow prediction.