According to the time series characteristics of the trajectory history data,we predicted and analyzed the traffic flow.This paper proposed a LSTMXGBoost model based urban road short-term traffic flow prediction in ord...According to the time series characteristics of the trajectory history data,we predicted and analyzed the traffic flow.This paper proposed a LSTMXGBoost model based urban road short-term traffic flow prediction in order to analyze and solve the problems of periodicity,stationary and abnormality of time series.It can improve the traffic flow prediction effect,achieve efficient traffic guidance and traffic control.The model combined the characteristics of LSTM(Long Short-Term Memory)network and XGBoost(Extreme Gradient Boosting)algorithms.First,we used the LSTM model that increases dropout layer to train the data set after preprocessing.Second,we replaced the full connection layer with the XGBoost model.Finally,we depended on the model training to strengthen the data association,avoided the overfitting phenomenon of the fully connected layer,and enhanced the generalization ability of the prediction model.We used the Kears based on TensorFlow to build the LSTM-XGBoost model.Using speed data samples of multiple road sections in Shenzhen to complete the model verification,we achieved the comparison of the prediction effects of the model.The results show that the combined prediction model used in this paper can not only improve the accuracy of prediction,but also improve the practicability,real-time and scalability of the model.展开更多
The metal organic framework functionalized with sulfonic acid was combined with magnetic nanoparticles to fabricate a new nanocomposite(denoted as Fe3O4@PDA@Zr-SO3H).By combining with gas chromatography-electron captu...The metal organic framework functionalized with sulfonic acid was combined with magnetic nanoparticles to fabricate a new nanocomposite(denoted as Fe3O4@PDA@Zr-SO3H).By combining with gas chromatography-electron capture detector,the resulting Fe3O4@PDA@Zr-SO3H nanocomposite was successfully used as a high-efficiency adsorbent for pre-concentrating eight organochlorine pesticides from water sample in environment.Apart from the ability of fast separation,the as-prepared Fe3O4@PDA@Zr-SO3H nanocomposite also exhibited high adsorption capacity for organochlorine pesticides.With the use of optimal experimental conditions,the linear relationship can be obtained in the range of 0.05~300μg/L,the correlation coefficient was over 0.9978,and the relative standard deviation was located in 2.5%-7.7%.Moreover,the limit of detection and quantification was between0.005-0.016μg/L and 0.017~0.050μg/L.Finally,the nanocomposite was used for the determination of organochlorine pesticides from environmental water samples,and displayed the recovery of 82%-118%.展开更多
基金The authors would like to thank the National Natural Science Foundation of China(61461027)National Natural Science Foundation of China(61465007)for financial support.
文摘According to the time series characteristics of the trajectory history data,we predicted and analyzed the traffic flow.This paper proposed a LSTMXGBoost model based urban road short-term traffic flow prediction in order to analyze and solve the problems of periodicity,stationary and abnormality of time series.It can improve the traffic flow prediction effect,achieve efficient traffic guidance and traffic control.The model combined the characteristics of LSTM(Long Short-Term Memory)network and XGBoost(Extreme Gradient Boosting)algorithms.First,we used the LSTM model that increases dropout layer to train the data set after preprocessing.Second,we replaced the full connection layer with the XGBoost model.Finally,we depended on the model training to strengthen the data association,avoided the overfitting phenomenon of the fully connected layer,and enhanced the generalization ability of the prediction model.We used the Kears based on TensorFlow to build the LSTM-XGBoost model.Using speed data samples of multiple road sections in Shenzhen to complete the model verification,we achieved the comparison of the prediction effects of the model.The results show that the combined prediction model used in this paper can not only improve the accuracy of prediction,but also improve the practicability,real-time and scalability of the model.
文摘The metal organic framework functionalized with sulfonic acid was combined with magnetic nanoparticles to fabricate a new nanocomposite(denoted as Fe3O4@PDA@Zr-SO3H).By combining with gas chromatography-electron capture detector,the resulting Fe3O4@PDA@Zr-SO3H nanocomposite was successfully used as a high-efficiency adsorbent for pre-concentrating eight organochlorine pesticides from water sample in environment.Apart from the ability of fast separation,the as-prepared Fe3O4@PDA@Zr-SO3H nanocomposite also exhibited high adsorption capacity for organochlorine pesticides.With the use of optimal experimental conditions,the linear relationship can be obtained in the range of 0.05~300μg/L,the correlation coefficient was over 0.9978,and the relative standard deviation was located in 2.5%-7.7%.Moreover,the limit of detection and quantification was between0.005-0.016μg/L and 0.017~0.050μg/L.Finally,the nanocomposite was used for the determination of organochlorine pesticides from environmental water samples,and displayed the recovery of 82%-118%.
