With the advancement of artificial intelligence,traffic forecasting is gaining more and more interest in optimizing route planning and enhancing service quality.Traffic volume is an influential parameter for planning ...With the advancement of artificial intelligence,traffic forecasting is gaining more and more interest in optimizing route planning and enhancing service quality.Traffic volume is an influential parameter for planning and operating traffic structures.This study proposed an improved ensemble-based deep learning method to solve traffic volume prediction problems.A set of optimal hyperparameters is also applied for the suggested approach to improve the performance of the learning process.The fusion of these methodologies aims to harness ensemble empirical mode decomposition’s capacity to discern complex traffic patterns and long short-term memory’s proficiency in learning temporal relationships.Firstly,a dataset for automatic vehicle identification is obtained and utilized in the preprocessing stage of the ensemble empirical mode decomposition model.The second aspect involves predicting traffic volume using the long short-term memory algorithm.Next,the study employs a trial-and-error approach to select a set of optimal hyperparameters,including the lookback window,the number of neurons in the hidden layers,and the gradient descent optimization.Finally,the fusion of the obtained results leads to a final traffic volume prediction.The experimental results show that the proposed method outperforms other benchmarks regarding various evaluation measures,including mean absolute error,root mean squared error,mean absolute percentage error,and R-squared.The achieved R-squared value reaches an impressive 98%,while the other evaluation indices surpass the competing.These findings highlight the accuracy of traffic pattern prediction.Consequently,this offers promising prospects for enhancing transportation management systems and urban infrastructure planning.展开更多
As one of the key technologies of intelligent transportation systems, short-term traffic volume prediction plays an increasingly important role in solving urban traffic problems. In the last decade, many approaches we...As one of the key technologies of intelligent transportation systems, short-term traffic volume prediction plays an increasingly important role in solving urban traffic problems. In the last decade, many approaches were proposed for the traffic volume prediction from different perspectives. However, most of these approaches are based on a large amount of historical data. When there are only finite collected traffic data, they cannot be well trained, so the prediction accuracy of these approaches will be poor. In this paper, a tensor model is proposed to capture the change patterns of continuous traffic volumes. From collected traffic volume data, the element data are extracted to update the corresponding elements of the tensor model. Then, a tucker decomposition and gradient descent based algorithm is employed to impute the missing elements of the tensor model. After missing element imputation, the tensor model can be directly applied to the short-term traffic volume prediction through searching the corresponding elements of the model and the storage cost of the model is low. Our model is evaluated on real traffic volume data from PeMS dataset, which indicates that our model has higher traffic volume prediction accuracy than other approaches in the situation of finite traffic volume data.展开更多
Based on the relationships between the lanes of signal-controlled intersections, we utilize the integration method of cluster analysis and stepwise regression and the integration method of cluster analysis and the pri...Based on the relationships between the lanes of signal-controlled intersections, we utilize the integration method of cluster analysis and stepwise regression and the integration method of cluster analysis and the principal component analysis method to predict the lanes＇ traffic volume of non-detector isolated controlled intersections. The results are examined by the real-time lanes＇ traffic volume data of the road network of Nanjing City. The problem of the lanes＇ traffic volume prediction of non-detector isolated signal-controlled intersections was resolved which can be widely used in urban traffic flow guidance and urban traffic control in cities.展开更多
In this study,a model based on multiple regression analysis is developed to forecast the tourism traffic volume of theme parks.First,the macro,meso and micro factors affecting traffic passenger volume are analysed.Sec...In this study,a model based on multiple regression analysis is developed to forecast the tourism traffic volume of theme parks.First,the macro,meso and micro factors affecting traffic passenger volume are analysed.Second,SPSS software is used for multivariate regression analysis on data for 10 theme parks from 2014.A tourism traffic volume forecasting model is then proposed.Finally,related data for 2015 is used to validate the model,with results showing a prediction error of 14.1%.All results show that the model has a high predictive ability.展开更多
文摘With the advancement of artificial intelligence,traffic forecasting is gaining more and more interest in optimizing route planning and enhancing service quality.Traffic volume is an influential parameter for planning and operating traffic structures.This study proposed an improved ensemble-based deep learning method to solve traffic volume prediction problems.A set of optimal hyperparameters is also applied for the suggested approach to improve the performance of the learning process.The fusion of these methodologies aims to harness ensemble empirical mode decomposition’s capacity to discern complex traffic patterns and long short-term memory’s proficiency in learning temporal relationships.Firstly,a dataset for automatic vehicle identification is obtained and utilized in the preprocessing stage of the ensemble empirical mode decomposition model.The second aspect involves predicting traffic volume using the long short-term memory algorithm.Next,the study employs a trial-and-error approach to select a set of optimal hyperparameters,including the lookback window,the number of neurons in the hidden layers,and the gradient descent optimization.Finally,the fusion of the obtained results leads to a final traffic volume prediction.The experimental results show that the proposed method outperforms other benchmarks regarding various evaluation measures,including mean absolute error,root mean squared error,mean absolute percentage error,and R-squared.The achieved R-squared value reaches an impressive 98%,while the other evaluation indices surpass the competing.These findings highlight the accuracy of traffic pattern prediction.Consequently,this offers promising prospects for enhancing transportation management systems and urban infrastructure planning.
基金supported by the National Natural Science Foundation of China(No.62276011,62072016)the Natural Science Foundation of Beijing Municipality(No.4212016)Urban Carbon Neutral Science and Technology Innovation Fund Project of Beijing University of Technology(No.040000514122608).
文摘As one of the key technologies of intelligent transportation systems, short-term traffic volume prediction plays an increasingly important role in solving urban traffic problems. In the last decade, many approaches were proposed for the traffic volume prediction from different perspectives. However, most of these approaches are based on a large amount of historical data. When there are only finite collected traffic data, they cannot be well trained, so the prediction accuracy of these approaches will be poor. In this paper, a tensor model is proposed to capture the change patterns of continuous traffic volumes. From collected traffic volume data, the element data are extracted to update the corresponding elements of the tensor model. Then, a tucker decomposition and gradient descent based algorithm is employed to impute the missing elements of the tensor model. After missing element imputation, the tensor model can be directly applied to the short-term traffic volume prediction through searching the corresponding elements of the model and the storage cost of the model is low. Our model is evaluated on real traffic volume data from PeMS dataset, which indicates that our model has higher traffic volume prediction accuracy than other approaches in the situation of finite traffic volume data.
文摘Based on the relationships between the lanes of signal-controlled intersections, we utilize the integration method of cluster analysis and stepwise regression and the integration method of cluster analysis and the principal component analysis method to predict the lanes＇ traffic volume of non-detector isolated controlled intersections. The results are examined by the real-time lanes＇ traffic volume data of the road network of Nanjing City. The problem of the lanes＇ traffic volume prediction of non-detector isolated signal-controlled intersections was resolved which can be widely used in urban traffic flow guidance and urban traffic control in cities.
基金supported by the Zhejiang Provincial Natural Science Foundation of China(Grant No.LY15E080008)the National Natural Science Foundation of China(Grant No.50908205).
文摘In this study,a model based on multiple regression analysis is developed to forecast the tourism traffic volume of theme parks.First,the macro,meso and micro factors affecting traffic passenger volume are analysed.Second,SPSS software is used for multivariate regression analysis on data for 10 theme parks from 2014.A tourism traffic volume forecasting model is then proposed.Finally,related data for 2015 is used to validate the model,with results showing a prediction error of 14.1%.All results show that the model has a high predictive ability.
