The invention discloses a traffic density prediction method based on a Bayesian network improved LSTM (Long Short Term Memory) model. The traffic density prediction method specifically comprises the following steps: acquiring historical data of road section traffic density; constructing a traffic density time sequence by using the historical traffic density data according to a time sequence; taking the time sequence as a sample to train an LSTM model, and obtaining a traffic density prediction model; and inputting the traffic density data and the time data of the predicted road section into the traffic density prediction model to obtain a prediction result. According to the method, the improved LSTM model based on the Bayesian network is adopted, historical traffic density big data can be used as a training set for training, and accurate prediction of future road section traffic density is achieved.
本发明公开了一种基于贝叶斯网络改进LSTM模型的交通密度预测方法,具体为:获取路段交通密度的历史数据;将历史交通密度数据按照时间顺序构建出交通密度时序序列;将时序序列作为样本训练LSTM模型,获得交通密度预测模型;将预测路段的交通密度数据和时间数据输入交通密度预测模型得到预测结果。本发明采用基于贝叶斯网络改进的LSTM模型,可以将历史交通密度大数据作为训练集进行训练,实现对未来路段交通密度的准确预测。
Traffic density prediction method based on Bayesian network improved LSTM model
基于贝叶斯网络改进LSTM模型的交通密度预测方法
2023-01-17
Patent
Electronic Resource
Chinese
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