Deep learning and big data techniques have become increasingly popular in traffic flow forecasting. Deep neural networks have also been applied to traffic flow forecasting. Furthermore, it is difficult to determine whether neural networks can be used for accurate traffic flow prediction. Moreover, since the network model is poorly structured and the parameter optimization technique is inappropriate, the traffic flow prediction is inaccurate because of the lack of certainty. The proposed system overcomes these problems by combining multiple simple recurrent long short-term memory (LSTM) neural networks with time traits to predict traffic flow using a deep gated stacked neural network. To deepen the model, the hidden layers have been trained using an unsupervised layer-by-layer approach. This approach provides a systematic representation of the time series data. A systematic representation of hidden layers improves the accuracy of time series forecasting by capturing information at multiple levels. Furthermore, it emphasizes the importance of model structure, random weight initialization, and hyperparameters used in stacked LSTM to enhance predictive performance. The prediction efficacy of the deep gated stacked LSTM model is compared with that of the gated recurrent unit model and the stacked autoencoder model.

中文翻译：

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DGSLSTM：用于大数据环境下交通网络交通流量预测的深度门控堆叠长短期记忆神经网络

深度学习和大数据技术在交通流预测中变得越来越流行。深度神经网络也被应用于交通流量预测。此外，很难确定神经网络是否可以用于准确的交通流量预测。此外，由于网络模型结构差，参数优化技术不合适，导致交通流预测不准确，缺乏确定性。所提出的系统通过将多个简单的循环长短期记忆 (LSTM) 神经网络与时间特征相结合来使用深度门控堆叠神经网络预测交通流量，从而克服了这些问题。为了加深模型，隐藏层已使用无监督的逐层方法进行训练。这种方法提供了时间序列数据的系统表示。隐藏层的系统表示通过捕获多个级别的信息来提高时间序列预测的准确性。此外，它强调了模型结构、随机权重初始化和堆叠 LSTM 中使用的超参数对提高预测性能的重要性。将深度门控堆叠 LSTM 模型的预测效果与门控循环单元模型和堆叠自动编码器模型的预测效果进行了比较。