节假日高速公路短时交通流预测
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摘要
针对高速公路愈加拥堵问题,提出了一种新的高速公路交通流预测方法,采用改进萤火虫算法优化RBF神经网络进行求解,结合历史节假日高速公路交通流数据进行训练和预测,与RBF神经网络预测结果进行对比,改进萤火虫算法优化RBF神经网络具有更好的预测结果,能够为高速公路管理中心提供稳定可靠的数据来源,为交管部门合理诱导疏散,缓解交通拥堵提供有效的技术支持。
With the free access of highway on holidays, the traffic flow of expressway is getting larger and larger. In order to solve the problem of highway congestion, this paper proposes a new highway traffic flow prediction method, with the improved firefly algorithm to optimize the RBF neural network in combination with the history of the holiday highway traffic flow data for training and prediction. Compared with RBF neural network prediction results, the improved firefly algorithm for optimizing RBF neural network has better prediction results. It can provide stable and reliable data source for expressway management center, and provide effective technical support for traffic management department to induce evacuation reasonably and alleviate traffic congestion.
With the free access of highway on holidays, the traffic flow of expressway is getting larger and larger. In order to solve the problem of highway congestion, this paper proposes a new highway traffic flow prediction method, with the improved firefly algorithm to optimize the RBF neural network in combination with the history of the holiday highway traffic flow data for training and prediction. Compared with RBF neural network prediction results, the improved firefly algorithm for optimizing RBF neural network has better prediction results. It can provide stable and reliable data source for expressway management center, and provide effective technical support for traffic management department to induce evacuation reasonably and alleviate traffic congestion.
引文
[1] Williams B M,Durvasula P K,Brown E.Urban freeway traffic flow prediction:application of seasonal autoregressive integrated moving average and exponential smoothing modles[J].Transportation Research Record 1644,1988:132-141.
[2] Abdulhai B,Porwal H,Pecker.Short-term Traffic Flow Prediction using Neural-Genetic Algorithms.ITS Journal,2002,7(1):3-41.
[3] 陈丽朝.BP神经网络在高速公路短时交通流预测中的应用[J].信息与电脑,2016(22):183-184.
[4] 张亮亮,贾元华,牛忠海,等.引入变化趋势参数的短时交通流预测研究[J].长安大学学报(自然科学版),2015(S1):170-173.
[5] 张玉梅,曲仕茹,温凯歌.基于混沌和RBF神经网络的短时交通流量预测[J].系统工程,2007,25(11):26-30.
[6] 刘长平,叶春明.一种新颖的仿生群智能优化算法:萤火虫算法[J].计算机应用研究,2011,28(9):3295-3297.
[7] 王改革,郭立红,段红,等.基于萤火虫算法优化BP神经网络的目标威胁估计[J].吉林大学学报(工学版),2013,43(4):1064-1069.
[8] 陈敏,刘文华.基于混沌理论的高速公路交通流预测研究[J].科学技术与工程,2009,9(2):478-481.
[9] 姚智胜,熊志华,邵春福.交通流短时预测的相关性研究[J].交通运输系统工程与信息,2010,10(2):117-121.
[10] 李贞珍.神经网络在城市交通流预测模型中的应用的研究[D].上海:上海交通大学硕士论文,2007.
[11] 孙湘海,刘潭秋.基于非线性时间序列模型的城市道路短期交通流预测研究 [J].土木工程学报,2008,41(1):104-109.
[2] Abdulhai B,Porwal H,Pecker.Short-term Traffic Flow Prediction using Neural-Genetic Algorithms.ITS Journal,2002,7(1):3-41.
[3] 陈丽朝.BP神经网络在高速公路短时交通流预测中的应用[J].信息与电脑,2016(22):183-184.
[4] 张亮亮,贾元华,牛忠海,等.引入变化趋势参数的短时交通流预测研究[J].长安大学学报(自然科学版),2015(S1):170-173.
[5] 张玉梅,曲仕茹,温凯歌.基于混沌和RBF神经网络的短时交通流量预测[J].系统工程,2007,25(11):26-30.
[6] 刘长平,叶春明.一种新颖的仿生群智能优化算法:萤火虫算法[J].计算机应用研究,2011,28(9):3295-3297.
[7] 王改革,郭立红,段红,等.基于萤火虫算法优化BP神经网络的目标威胁估计[J].吉林大学学报(工学版),2013,43(4):1064-1069.
[8] 陈敏,刘文华.基于混沌理论的高速公路交通流预测研究[J].科学技术与工程,2009,9(2):478-481.
[9] 姚智胜,熊志华,邵春福.交通流短时预测的相关性研究[J].交通运输系统工程与信息,2010,10(2):117-121.
[10] 李贞珍.神经网络在城市交通流预测模型中的应用的研究[D].上海:上海交通大学硕士论文,2007.
[11] 孙湘海,刘潭秋.基于非线性时间序列模型的城市道路短期交通流预测研究 [J].土木工程学报,2008,41(1):104-109.