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梁秀霞,李万通,杨凡,等. 基于改进天牛须算法优化的确定性跳跃循环状态网络的交通流预测[J]. 科学技术与工程, 2021, 21(8): 3372-3378.
liangxiuxia,et al.Prediction of Short-term Traffic Flow Based on Phase Space Reconstruction and Improved BAS-CRJ[J].Science Technology and Engineering,2021,21(8):3372-3378.
基于改进天牛须算法优化的确定性跳跃循环状态网络的交通流预测
Prediction of Short-term Traffic Flow Based on Phase Space Reconstruction and Improved BAS-CRJ
投稿时间:2020-06-08  修订日期:2020-12-15
DOI:
中文关键词:  智能交通  交通流速度预测  混沌理论  相空间重构  天牛须算法  回声状态网络
英文关键词:intelligent transportation  traffic flow speed prediction  chaos theory  phase space reconstruction  Beetle Antennae Search Algorithm  Echo state network
基金项目:国家自然科学基金项目(61773151),河北省自然科学基金(F2018202279)
           
作者单位
梁秀霞 河北工业大学
李万通 河北工业大学
杨凡 河北工业大学
张燕 河北工业大学
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中文摘要:
      为了提高短时交通流速度预测的精度,提出了一种基于相空间重构和改进BAS-CRJ的短时交通流速度预测模型。首先对交通流速度序列进行浑沌性分析,重构序列的相空间,将对交通流速度序列的研究映射到其所在的相空间中进行;然后引入变步长因子和模拟退火技术对天牛须算法(BAS)进行改进,并以改进天牛须算法优化确定性跳跃循环神经网络(CRJ)的参数,构建预测模型;最后通过实例对比分析了模型的有效性。结果表明,通过相空间重构对交通流速度序列处理,能够挖掘序列内部的动态特性,使之更加适用于网络的建模;所提模型的预测结果同对比模型相比,平均绝对百分比误差下降了1.05%~6.04%,有效地提高了短时交通流速度的预测精度。
英文摘要:
      In order to improve the prediction accuracy of short-term traffic flow, a short-term traffic flow prediction method based on phase space reconstruction and improved BAS-CRJ is proposed.First, the chaotic analysis of the traffic flow speed sequence is carried out, the phase space of the sequence is reconstructed, and the research on the traffic flow speed sequence is mapped into the phase space where it is located. Then, the variable step size factor and simulated annealing technology are introduced to improve Beetle Antennae Search Algorithm(BAS), and the improved Beetle Antennae Search Algorithm is used to optimize the parameters of the cycle reservoir with regular jumps network (CRJ). A forecast model is developed. Finally, the effectiveness of the model is analyzed through an example. The results show that the internal dynamic characteristics of the traffic flow speed sequence are tapped through phase space reconstruction, which is more suitable for network modeling. And the prediction result of the proposed model are better than comparison models, in which the average absolute percentage error is decreased by 1.05%~6.04%. The prediction accuracy of short-term traffic flow speed is effectively improved.
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