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
为实现准确的机场流量短期预测,本文建立了基于二次分解方法的分解集成预测模型。首先,应用局部加权回归周期趋势分解(STL,seasonal and trend decomposition procedure based on Loess)算法将原始时间序列分解为趋势项、季节项和余项3...为实现准确的机场流量短期预测,本文建立了基于二次分解方法的分解集成预测模型。首先,应用局部加权回归周期趋势分解(STL,seasonal and trend decomposition procedure based on Loess)算法将原始时间序列分解为趋势项、季节项和余项3个分量,并计算其样本熵。其次,应用遗传算法(GA,genetic algorithm)优化变分模态分解(VMD,variational mode decomposition)参数,对熵值较大的分量进行二次分解。再次,使用极端梯度提升(XGBoost,extreme gradient boosting)对二次分解后的所有分量进行预测,采用加和集成得到最终的预测值。最后,采集国内典型机场实际运行数据进行实例分析。针对北京首都国际机场60 min进场、离场流量时序,本文模型预测的均等系数(EC,equal coefficient)值分别为0.9703、0.9959,相比其他常用模型均有所提高。此外,对于上海浦东、上海虹桥、广州白云3个大型国际机场,本文模型在60 min、30 min统计尺度下进场和离场流量预测的EC值均在0.9700以上,15 min统计尺度下预测的EC值均在0.9500以上。结果表明,本文建立的二次分解集成预测模型具有良好的准确性和普适性,用于机场流量短期预测是可行和有效的。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
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.
