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
Prompt and accurate traffic flow forecasting is a key foundation of urban traffic management.However,the flows in different areas and feature channels(inflow/outflow)may correspond to different degrees of importance i...Prompt and accurate traffic flow forecasting is a key foundation of urban traffic management.However,the flows in different areas and feature channels(inflow/outflow)may correspond to different degrees of importance in forecasting flows.Many forecasting models inadequately consider this heterogeneity,resulting in decreased predictive accuracy.To overcome this problem,an attention-based hybrid spatiotemporal residual model assisted by spatial and channel information is proposed in this study.By assigning different weights(attention levels)to different regions,the spatial attention module selects relatively important locations from all inputs in the modeling process.Similarly,the channel attention module selects relatively important channels from the multichannel feature map in the modeling process by assigning different weights.The proposed model provides effective selection and attention results for key areas and channels,respectively,during the forecasting process,thereby decreasing the computational overhead and increasing the accuracy.In the case involving Beijing,the proposed model exhibits a 3.7%lower prediction error,and its runtime is 60.9%less the model without attention,indicating that the spatial and channel attention modules are instrumental in increasing the forecasting efficiency.Moreover,in the case involving Shanghai,the proposed model outperforms other models in terms of generalizability and practicality.展开更多
With the urbanization,urban transportation has become a key factor restricting the development of a city.In a big city,it is important to improve the efficiency of urban transportation.The key to realize short-term tr...With the urbanization,urban transportation has become a key factor restricting the development of a city.In a big city,it is important to improve the efficiency of urban transportation.The key to realize short-term traffic flow prediction is to learn its complex spatial correlation,temporal correlation and randomness of traffic flow.In this paper,the convolution neural network(CNN)is proposed to deal with spatial correlation among different regions,considering that the large urban areas leads to a relatively deep Network layer.First three gated recurrent unit(GRU)were used to deal with recent time dependence,daily period dependence and weekly period dependence.Considering that each historical period data to forecast the influence degree of the time period is different,three attention mechanism was taken into GRU.Second,a twolayer full connection network was applied to deal with the randomness of short-term flow combined with additional information such as weather data.Besides,the prediction model was established by combining these three modules.Furthermore,in order to verify the influence of spatial correlation on prediction model,an urban functional area identification model was introduced to identify different functional regions.Finally,the proposed model was validated based on the history of New York City taxi order data and reptiles for weather data.The experimental results show that the prediction precision of our model is obviously superior to the mainstream of the existing prediction methods.展开更多
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.
基金supported by National Key R&D Program of China:[grant number 2017YFB0503605].
文摘Prompt and accurate traffic flow forecasting is a key foundation of urban traffic management.However,the flows in different areas and feature channels(inflow/outflow)may correspond to different degrees of importance in forecasting flows.Many forecasting models inadequately consider this heterogeneity,resulting in decreased predictive accuracy.To overcome this problem,an attention-based hybrid spatiotemporal residual model assisted by spatial and channel information is proposed in this study.By assigning different weights(attention levels)to different regions,the spatial attention module selects relatively important locations from all inputs in the modeling process.Similarly,the channel attention module selects relatively important channels from the multichannel feature map in the modeling process by assigning different weights.The proposed model provides effective selection and attention results for key areas and channels,respectively,during the forecasting process,thereby decreasing the computational overhead and increasing the accuracy.In the case involving Beijing,the proposed model exhibits a 3.7%lower prediction error,and its runtime is 60.9%less the model without attention,indicating that the spatial and channel attention modules are instrumental in increasing the forecasting efficiency.Moreover,in the case involving Shanghai,the proposed model outperforms other models in terms of generalizability and practicality.
基金the Natural Science Foundation of China grant61672128, 61702076the Fundamental Research Funds for the Central UniversitiesDUT18JC39.
文摘With the urbanization,urban transportation has become a key factor restricting the development of a city.In a big city,it is important to improve the efficiency of urban transportation.The key to realize short-term traffic flow prediction is to learn its complex spatial correlation,temporal correlation and randomness of traffic flow.In this paper,the convolution neural network(CNN)is proposed to deal with spatial correlation among different regions,considering that the large urban areas leads to a relatively deep Network layer.First three gated recurrent unit(GRU)were used to deal with recent time dependence,daily period dependence and weekly period dependence.Considering that each historical period data to forecast the influence degree of the time period is different,three attention mechanism was taken into GRU.Second,a twolayer full connection network was applied to deal with the randomness of short-term flow combined with additional information such as weather data.Besides,the prediction model was established by combining these three modules.Furthermore,in order to verify the influence of spatial correlation on prediction model,an urban functional area identification model was introduced to identify different functional regions.Finally,the proposed model was validated based on the history of New York City taxi order data and reptiles for weather data.The experimental results show that the prediction precision of our model is obviously superior to the mainstream of the existing prediction methods.
