With the exponential growth of traffic data and the complexity of traffic conditions, in order to effectively store and analyse data to feed back valid information, this paper proposed an urban road traffic status prediction model based on the optimized deep recurrent Q-Learning method. The model is based on the optimized Long Short-Term Memory (LSTM) algorithm to handle the explosive growth of Q-table data, which not only avoids the gradient explosion and disappearance but also has the efficient storage and analysis. The continuous training and memory storage of the training sets are used to improve the system sensitivity, and then, the test sets are predicted based on the accumulated experience pool to obtain high-precision prediction results. The traffic flow data from Wanjiali Road to Shuangtang Road in Changsha City are tested as a case. The research results show that the prediction of the traffic delay index is within a reasonable interval, and it is significantly better than traditional prediction methods such as the LSTM, K-Nearest Neighbor (KNN), Support Vector Machines (SVM), exponential smoothing method, and Back Propagation (BP) neural network, which shows that the model proposed in this paper has the feasibility of application.

中文翻译：

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基于深度递归Q学习的主干道交通状态预测

随着交通数据的指数增长和交通条件的复杂性，为了有效地存储和分析数据以反馈有效信息，本文提出了一种基于优化的深度递归Q学习方法的城市道路交通状况预测模型。该模型基于优化的长短期记忆（LSTM）算法来处理Q表数据的爆炸性增长，不仅避免了梯度爆炸和消失，而且具有高效的存储和分析能力。利用训练集的连续训练和记忆存储来提高系统的敏感性，然后基于积累的经验库对测试集进行预测，以获得高精度的预测结果。以长沙市万家里路至双塘路的交通流量数据为例。