This paper focuses on time series forecasting of monthly occurrence of fatal road accidents in Ondo State of Nigeria. Its aim, however, is to use time series analysis to analyze the data obtained from Federal Road Saf...This paper focuses on time series forecasting of monthly occurrence of fatal road accidents in Ondo State of Nigeria. Its aim, however, is to use time series analysis to analyze the data obtained from Federal Road Safety Corps （FRSC）, Ondo State Command; which was considered in two cases： the total cases reported （TCR） and the number of deaths resulted from accidents （NOD）. Various smoothing models for time series were used to analyze the two cases. Based on the models, predictions were made and the results show a steady increase as a result of long-term effects on road accidents for the two cases. It was found also that simple exponential smoothing model is the appropriate model for both TCR and NOD.展开更多
Considering the instability of the output power of photovoltaic(PV)generation system,to improve the power regulation ability of PV power during grid-connected operation,based on the quantitative analysis of meteorolog...Considering the instability of the output power of photovoltaic(PV)generation system,to improve the power regulation ability of PV power during grid-connected operation,based on the quantitative analysis of meteorological conditions,a short-term prediction method of PV power based on LMD-EE-ESN with iterative error correction was proposed.Firstly,through the fuzzy clustering processing of meteorological conditions,taking the power curves of PV power generation in sunny,rainy or snowy,cloudy,and changeable weather as the reference,the local mean decomposition(LMD)was carried out respectively,and their energy entropy(EE)was taken as the meteorological characteristics.Then,the historical generation power series was decomposed by LMD algorithm,and the hierarchical prediction of the power curve was realized by echo state network(ESN)prediction algorithm combined with meteorological characteristics.Finally,the iterative error theory was applied to the correction of power prediction results.The analysis of the historical data in the PV power generation system shows that this method avoids the influence of meteorological conditions in the short-term prediction of PV output power,and improves the accuracy of power prediction on the condition of hierarchical prediction and iterative error correction.展开更多
The accurate and timely traffic state prediction has become increasingly important for the traffic participants,especially for the traffic managements. In this paper,the traffic state is described by Micro-LOS,and a d...The accurate and timely traffic state prediction has become increasingly important for the traffic participants,especially for the traffic managements. In this paper,the traffic state is described by Micro-LOS,and a direct prediction method is introduced. The development of the proposed method is based on Maximum Entropy (ME) models trained for multiple modes. In the Multimode Maximum Entropy (MME) framework,the different features like temporal and spatial features of traffic systems,regional traffic state are integrated simultaneously,and the different state behaviors based on 14 traffic modes defined by average speed according to the date-time division are also dealt with. The experiments based on the real data in Beijing expressway prove that the MME models outperforms the already existing model in both effectiveness and robustness.展开更多
The back-propagation neural network(BPNN) is a well-known multi-layer feed-forward neural network which is trained by the error reverse propagation algorithm. It is very suitable for the complex of short-term traffic ...The back-propagation neural network(BPNN) is a well-known multi-layer feed-forward neural network which is trained by the error reverse propagation algorithm. It is very suitable for the complex of short-term traffic flow forecasting; however, BPNN is easy to fall into local optimum and slow convergence. In order to overcome these deficiencies, a new approach called social emotion optimization algorithm(SEOA) is proposed in this paper to optimize the linked weights and thresholds of BPNN. Each individual in SEOA represents a BPNN. The availability of the proposed forecasting models is proved with the actual traffic flow data of the 2 nd Ring Road of Beijing. Experiment of results show that the forecasting accuracy of SEOA is improved obviously as compared with the accuracy of particle swarm optimization back-propagation(PSOBP) and simulated annealing particle swarm optimization back-propagation(SAPSOBP) models. Furthermore, since SEOA does not respond to the negative feedback information, Metropolis rule is proposed to give consideration to both positive and negative feedback information and diversify the adjustment methods. The modified BPNN model, in comparison with social emotion optimization back-propagation(SEOBP) model, is more advantageous to search the global optimal solution. The accuracy of Metropolis rule social emotion optimization back-propagation(MRSEOBP) model is improved about 19.54% as compared with that of SEOBP model in predicting the dramatically changing data.展开更多
文摘This paper focuses on time series forecasting of monthly occurrence of fatal road accidents in Ondo State of Nigeria. Its aim, however, is to use time series analysis to analyze the data obtained from Federal Road Safety Corps （FRSC）, Ondo State Command; which was considered in two cases： the total cases reported （TCR） and the number of deaths resulted from accidents （NOD）. Various smoothing models for time series were used to analyze the two cases. Based on the models, predictions were made and the results show a steady increase as a result of long-term effects on road accidents for the two cases. It was found also that simple exponential smoothing model is the appropriate model for both TCR and NOD.
基金supported by National Natural Science Foundation of China(No.516667017).
文摘Considering the instability of the output power of photovoltaic(PV)generation system,to improve the power regulation ability of PV power during grid-connected operation,based on the quantitative analysis of meteorological conditions,a short-term prediction method of PV power based on LMD-EE-ESN with iterative error correction was proposed.Firstly,through the fuzzy clustering processing of meteorological conditions,taking the power curves of PV power generation in sunny,rainy or snowy,cloudy,and changeable weather as the reference,the local mean decomposition(LMD)was carried out respectively,and their energy entropy(EE)was taken as the meteorological characteristics.Then,the historical generation power series was decomposed by LMD algorithm,and the hierarchical prediction of the power curve was realized by echo state network(ESN)prediction algorithm combined with meteorological characteristics.Finally,the iterative error theory was applied to the correction of power prediction results.The analysis of the historical data in the PV power generation system shows that this method avoids the influence of meteorological conditions in the short-term prediction of PV output power,and improves the accuracy of power prediction on the condition of hierarchical prediction and iterative error correction.
基金supported by Beijing Science Foundation Plan Project(Grant No.D07020601400707)the National High Technology Re-search and Development Program of China(Grant NO.2006AA11Z231)
文摘The accurate and timely traffic state prediction has become increasingly important for the traffic participants,especially for the traffic managements. In this paper,the traffic state is described by Micro-LOS,and a direct prediction method is introduced. The development of the proposed method is based on Maximum Entropy (ME) models trained for multiple modes. In the Multimode Maximum Entropy (MME) framework,the different features like temporal and spatial features of traffic systems,regional traffic state are integrated simultaneously,and the different state behaviors based on 14 traffic modes defined by average speed according to the date-time division are also dealt with. The experiments based on the real data in Beijing expressway prove that the MME models outperforms the already existing model in both effectiveness and robustness.
基金the Research of New Intelligent Integrated Transport Information System,Technical Plan Project of Binhai New District,Tianjin(No.2015XJR21017)
文摘The back-propagation neural network(BPNN) is a well-known multi-layer feed-forward neural network which is trained by the error reverse propagation algorithm. It is very suitable for the complex of short-term traffic flow forecasting; however, BPNN is easy to fall into local optimum and slow convergence. In order to overcome these deficiencies, a new approach called social emotion optimization algorithm(SEOA) is proposed in this paper to optimize the linked weights and thresholds of BPNN. Each individual in SEOA represents a BPNN. The availability of the proposed forecasting models is proved with the actual traffic flow data of the 2 nd Ring Road of Beijing. Experiment of results show that the forecasting accuracy of SEOA is improved obviously as compared with the accuracy of particle swarm optimization back-propagation(PSOBP) and simulated annealing particle swarm optimization back-propagation(SAPSOBP) models. Furthermore, since SEOA does not respond to the negative feedback information, Metropolis rule is proposed to give consideration to both positive and negative feedback information and diversify the adjustment methods. The modified BPNN model, in comparison with social emotion optimization back-propagation(SEOBP) model, is more advantageous to search the global optimal solution. The accuracy of Metropolis rule social emotion optimization back-propagation(MRSEOBP) model is improved about 19.54% as compared with that of SEOBP model in predicting the dramatically changing data.
