Predicting travel trajectory of vehicles can not only provide personalized services to users,but also have a certain effect on traffic guidance and traffic control.In this paper,we build a Bayonet-Corpus based on the ...Predicting travel trajectory of vehicles can not only provide personalized services to users,but also have a certain effect on traffic guidance and traffic control.In this paper,we build a Bayonet-Corpus based on the context of traffic intersections,and use it to model a traffic network.Besides,Bidirectional Gated Recurrent Unit(Bi-GRU)is used to predict the sequence of traffic intersections in one single trajectory.Firstly,considering that real traffic networks are usually complex and disorder and cannot reflect the higher dimensional relationship among traffic intersections,this paper proposes a new traffic network modeling algorithm based on the context of traffic intersections:inspired by the probabilistic language model,a Bayonet-Corpus is constructed from traffic intersections in real trajectory sequence,so the high-dimensional similarity between corpus nodes can be used to measure the semantic relation of real traffic intersections.This algorithm maps vehicle trajectory nodes into a high-dimensional space vector,blocking complex structure of real traffic network and reconstructing the traffic network space.Then,the bayonets sequence in real traffic network is mapped into a matrix.Considering the trajectories sequence is bidirectional,and Bi-GRU can handle information from forward and backward simultaneously,we use Bi-GRU to bidirectionally model the trajectory matrix for the purpose of prediction.展开更多
The transportation department relies on accurate traffic forecasting for effective decision-making.However,determining relevant parameters for existing traffic flow prediction models poses challenges.To address this i...The transportation department relies on accurate traffic forecasting for effective decision-making.However,determining relevant parameters for existing traffic flow prediction models poses challenges.To address this issue,this study proposes a hybrid model,sparrow search algorithm-gated recurrent unit-long short-term memory(SSA-GRU-LSTM),which leverages the SSA to optimize the GRUs and LSTM networks.The SSA is employed to identify the optimal parameters for the GRULSTM model,mitigating their impact on prediction accuracy.This model integrates the predictive efficiency of the GRU,LSTM’s capability in temporal data analysis,and the fast convergence and global search attributes of the SSA.Comprehensive experiments are conducted to validate the proposed method via traffic flow datasets,and the results are compared with those of baseline models.The numerical results demonstrate the superior performance of the SSA-GRU-LSTM model.Compared with the baselines,the proposed model results in reductions in the root mean square error(RMSE)of 4.632%-45.206%,the mean absolute error(MAE)of 2.608%-53.327%,the mean absolute percentage error(MAPE)of 1.324%-13.723%,and an increase in R^(2) of 0.5%-17.5%.Consequently,the SSA-GRU-LSTM model has high prediction accuracy and measurement stability.展开更多
Efficient and accurate wind power prediction is crucial for enhancing the reliability and safety of power system.The data-driven forecasting methods are regarded as an effective solution.However,the inherent randomnes...Efficient and accurate wind power prediction is crucial for enhancing the reliability and safety of power system.The data-driven forecasting methods are regarded as an effective solution.However,the inherent randomness and nonlinearity of wind power systems,along with the abundance of redundant information in measurement data,present challenges to forecasting methods.The integration of precise and efficient techniques for data feature decomposition and extraction is essential in conjunction with advanced driven data-forecasting models.Focus on the seasonal variation characteristics of wind energy,a hybrid wind power prediction model based on seasonal feature decomposition and enhanced feature extraction is proposed.The effectiveness and superiority of the proposed method in predictive accuracy are demonstrated through comprehensive multi-model experiment comparisons.展开更多
基金This research is partially supported by the National Natural Science Foundation of China(Grant No.61772098)Science and Technology Research Program of Chongqing Municipal Education Commission(Grant No.KJZD K201900603,KJQN201900629)Chongqing Grad-uate Education Teaching Reform Project(No.yjg183081).
文摘Predicting travel trajectory of vehicles can not only provide personalized services to users,but also have a certain effect on traffic guidance and traffic control.In this paper,we build a Bayonet-Corpus based on the context of traffic intersections,and use it to model a traffic network.Besides,Bidirectional Gated Recurrent Unit(Bi-GRU)is used to predict the sequence of traffic intersections in one single trajectory.Firstly,considering that real traffic networks are usually complex and disorder and cannot reflect the higher dimensional relationship among traffic intersections,this paper proposes a new traffic network modeling algorithm based on the context of traffic intersections:inspired by the probabilistic language model,a Bayonet-Corpus is constructed from traffic intersections in real trajectory sequence,so the high-dimensional similarity between corpus nodes can be used to measure the semantic relation of real traffic intersections.This algorithm maps vehicle trajectory nodes into a high-dimensional space vector,blocking complex structure of real traffic network and reconstructing the traffic network space.Then,the bayonets sequence in real traffic network is mapped into a matrix.Considering the trajectories sequence is bidirectional,and Bi-GRU can handle information from forward and backward simultaneously,we use Bi-GRU to bidirectionally model the trajectory matrix for the purpose of prediction.
基金supported by the Gansu Provincial Science and Technology Major Special Project-Enterprise Innovation Consortium Project(No.22ZD6GA010)the Natural Science Foundation of China(No.52062027)the Key Research and Development Project of Gansu Province(No.22YF7GA142).
文摘The transportation department relies on accurate traffic forecasting for effective decision-making.However,determining relevant parameters for existing traffic flow prediction models poses challenges.To address this issue,this study proposes a hybrid model,sparrow search algorithm-gated recurrent unit-long short-term memory(SSA-GRU-LSTM),which leverages the SSA to optimize the GRUs and LSTM networks.The SSA is employed to identify the optimal parameters for the GRULSTM model,mitigating their impact on prediction accuracy.This model integrates the predictive efficiency of the GRU,LSTM’s capability in temporal data analysis,and the fast convergence and global search attributes of the SSA.Comprehensive experiments are conducted to validate the proposed method via traffic flow datasets,and the results are compared with those of baseline models.The numerical results demonstrate the superior performance of the SSA-GRU-LSTM model.Compared with the baselines,the proposed model results in reductions in the root mean square error(RMSE)of 4.632%-45.206%,the mean absolute error(MAE)of 2.608%-53.327%,the mean absolute percentage error(MAPE)of 1.324%-13.723%,and an increase in R^(2) of 0.5%-17.5%.Consequently,the SSA-GRU-LSTM model has high prediction accuracy and measurement stability.
基金supported in part by National Natural Science Foundation of China(61803300,62371388).
文摘Efficient and accurate wind power prediction is crucial for enhancing the reliability and safety of power system.The data-driven forecasting methods are regarded as an effective solution.However,the inherent randomness and nonlinearity of wind power systems,along with the abundance of redundant information in measurement data,present challenges to forecasting methods.The integration of precise and efficient techniques for data feature decomposition and extraction is essential in conjunction with advanced driven data-forecasting models.Focus on the seasonal variation characteristics of wind energy,a hybrid wind power prediction model based on seasonal feature decomposition and enhanced feature extraction is proposed.The effectiveness and superiority of the proposed method in predictive accuracy are demonstrated through comprehensive multi-model experiment comparisons.
