Short-term traffic flow has the characteristics of complex, changeable, strong timeliness, and so on. So the traditional prediction algorithm is difficult to meet its high real-time and accuracy requirements. In this paper, a multiscale and high-precision LSTM-GASVR short-term traffic flow prediction algorithm is proposed. This method uses 15 min traffic flow data of the first 16 sections as input and completes the data preprocessing operation through reconstruction, normalization, and rising dimension by working day factor; establishing the prediction model based on the long- and short-term memory network (LSTM) and inverse normalization; and proposing the GA-SVR model to optimize the prediction results, so as to realize the real-time high-precision prediction of traffic flow. The prediction experiment is carried out according to the charge data of a toll station in Xi’an, Shaanxi Province, from May 2018 to May 2019. The comparison and analysis of various algorithms show that the prediction algorithm proposed in this paper is 20% higher than the LSTM, GRU, CNN, SAE, ARIMA, and SVR, and the R² can reach 0.982, the explanatory variance is 0.982, and the MAPE is 0.118. The proposed traffic flow prediction algorithm provides strong support for traffic managers to judge the state of the road network to control traffic and guide traffic flow.

中文翻译：

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多尺度，高精度LSTM-GASVR短期交通流量预测模型

短期交通流具有复杂，多变，及时性强等特点。因此传统的预测算法难以满足其较高的实时性和准确性要求。提出了一种多尺度，高精度的LSTM-GASVR短期交通流量预测算法。该方法以前16个路段的15分钟交通流量数据为输入，并通过工作日因子的重构，归一化和上升维数完成数据预处理操作。基于长期和短期记忆网络（LSTM）和逆归一化建立预测模型；提出了GA-SVR模型对预测结果进行优化，以实现交通流量的实时高精度预测。R ²可以达到0.982，解释方差为0.982，MAPE为0.118。所提出的交通流量预测算法为交通管理人员判断路网状态以控制交通和引导交通提供强有力的支持。