Traffic flow prediction,as the basis of signal coordination and travel time prediction,has become a research point in the field of transportation.For traffic flow prediction,researchers have proposed a variety of meth...Traffic flow prediction,as the basis of signal coordination and travel time prediction,has become a research point in the field of transportation.For traffic flow prediction,researchers have proposed a variety of methods,but most of these methods only use the time domain information of traffic flow data to predict the traffic flow,ignoring the impact of spatial correlation on the prediction of target road segment flow,which leads to poor prediction accuracy.In this paper,a traffic flow prediction model called as long short time memory and random forest(LSTM-RF)was proposed based on the combination model.In the process of traffic flow prediction,the long short time memory(LSTM)model was used to extract the time sequence features of the predicted target road segment.Then,the predicted value of LSTM and the collected information of adjacent upstream and downstream sections were simultaneously used as the input features of the random forest model to analyze the spatial-temporal correlation of traffic flow,so as to obtain the final prediction results.The traffic flow data of 132 urban road sections collected by the license plate recognition system in Guiyang City were tested and verified.The results show that the method is better than the single model in prediction accuracy,and the prediction error is obviously reduced compared with the single model.展开更多
目的 建立气象因素和空气污染物对死亡人数的预警预测模型,为公共卫生决策提供科学依据。方法 基于我国某地区2014—2018年死因及环境检测数据,首先采用Spearman和Boruta方法分析气象因素和空气污染物与死亡人数的相关性,筛选死亡人数...目的 建立气象因素和空气污染物对死亡人数的预警预测模型,为公共卫生决策提供科学依据。方法 基于我国某地区2014—2018年死因及环境检测数据,首先采用Spearman和Boruta方法分析气象因素和空气污染物与死亡人数的相关性,筛选死亡人数的关键因素。然后利用深度学习中的LSTM、BP神经网络提取关键因素的隐含特征,并将提取的隐含特征作为新的特征输入随机森林模型(RF),构建了LSTM-RF和BP-RF组合模型。结果 以RMSE、MAE、准确率、精确率、召回率、曲线下面积(area under the curve,AUC)值等指标作为评价标准,与RF、XGBoost、GBDT、BP、LSTM等单一模型进行对比。相比单一的机器学习和深度学习模型,该研究所构建的LSTM-RF和BP-RF组合模型各个评价指标均优于单一模型。LSTM-RF为最优模型,其在测试集上RMSE为2.93,AUC值高达0.941。结论 LSTM-RF更适合作为气象因素和空气污染的预警预测模型。展开更多
文摘Traffic flow prediction,as the basis of signal coordination and travel time prediction,has become a research point in the field of transportation.For traffic flow prediction,researchers have proposed a variety of methods,but most of these methods only use the time domain information of traffic flow data to predict the traffic flow,ignoring the impact of spatial correlation on the prediction of target road segment flow,which leads to poor prediction accuracy.In this paper,a traffic flow prediction model called as long short time memory and random forest(LSTM-RF)was proposed based on the combination model.In the process of traffic flow prediction,the long short time memory(LSTM)model was used to extract the time sequence features of the predicted target road segment.Then,the predicted value of LSTM and the collected information of adjacent upstream and downstream sections were simultaneously used as the input features of the random forest model to analyze the spatial-temporal correlation of traffic flow,so as to obtain the final prediction results.The traffic flow data of 132 urban road sections collected by the license plate recognition system in Guiyang City were tested and verified.The results show that the method is better than the single model in prediction accuracy,and the prediction error is obviously reduced compared with the single model.
文摘目的 建立气象因素和空气污染物对死亡人数的预警预测模型,为公共卫生决策提供科学依据。方法 基于我国某地区2014—2018年死因及环境检测数据,首先采用Spearman和Boruta方法分析气象因素和空气污染物与死亡人数的相关性,筛选死亡人数的关键因素。然后利用深度学习中的LSTM、BP神经网络提取关键因素的隐含特征,并将提取的隐含特征作为新的特征输入随机森林模型(RF),构建了LSTM-RF和BP-RF组合模型。结果 以RMSE、MAE、准确率、精确率、召回率、曲线下面积(area under the curve,AUC)值等指标作为评价标准,与RF、XGBoost、GBDT、BP、LSTM等单一模型进行对比。相比单一的机器学习和深度学习模型,该研究所构建的LSTM-RF和BP-RF组合模型各个评价指标均优于单一模型。LSTM-RF为最优模型,其在测试集上RMSE为2.93,AUC值高达0.941。结论 LSTM-RF更适合作为气象因素和空气污染的预警预测模型。
