基于IFA优化RBF神经网络的短时交通流预测模型

兰州理工大学学报 ›› 2022, Vol. 48 ›› Issue (4): 99-104.

• 自动化技术与计算机技术 • 上一篇下一篇

基于IFA优化RBF神经网络的短时交通流预测模型

曹洁^*1,2,3, 张敏¹, 张红^1,2, 陈作汉^1,2, 侯亮^1,2,3

1.兰州理工大学计算机与通信学院, 甘肃兰州 730050;
2.甘肃省城市轨道交通智能运营工程研究中心, 甘肃兰州 730050;
3.甘肃省制造业信息化工程研究中心, 甘肃兰州 730050

收稿日期:2020-11-04 出版日期:2022-08-28 发布日期:2022-10-09
通讯作者: 曹洁(1966-),女,安徽宿州人,教授,博导.Email:caoj@lut.edu.cn
基金资助:
国家自然科学基金(61763028),甘肃省自然科学基金(20JR5RA450)

Short-term traffic flow prediction model based on IFA optimized RBF neural network

CAO Jie^1,2,3, ZHANG Min¹, ZHANG Hong^1,2, CHEN Zuo-han^1,2, HOU Liang^1,2,3

1. School of Computer and Communication, Lanzhou Univ. of Tech., Lanzhou 730050, China;
2. Engineering Research Center of Urban Railway Transportation of Gansu Province, Lanzhou 730050, China;
3. Engineering Research Center of Manufacturing Information of Gansu Province, Lanzhou 730050, China

Received:2020-11-04 Online:2022-08-28 Published:2022-10-09

摘要/Abstract

摘要： 针对短时交通流不确定性极强引起的预测结果精度低的问题,提出一种改进萤火虫算法(IFA)优化RBF神经网络的短时交通流预测模型(IFA-RBF).该模型通过引入线性递减惯性权重和混沌机制,来改进FA后期存在的易陷入局部极值和种群多样性匮乏的不足,利用IFA优化RBF神经网络的连接权重和基函数宽度,以提升RBF神经网络的短时交通流预测精度.实验结果表明,与Elman、BP、RBF和FA-RBF模型相比,构建的短时交通流预测模型(IFA-RBF)具有更高的预测精度,预测值与实际值拟合度较高.

关键词: 智能交通系统, 交通流, 短时预测, RBF神经网络, 改进的萤火虫算法, 混沌搜索

Abstract: Aiming at the problem of low accuracy of prediction results caused by the extremely strong uncertainty of short-term traffic flow data, a short-term traffic flow prediction model(IFA-RBF) based on the improved firefly algorithm (IFA) optimized RBF neural network is proposed. By introducing linear decreasing inertial weight and chaos mechanism, the model improves the shortcoming of being trapped into local extremes and lack of population diversity in the later stage of FA. IFA is used to optimize the connection weight and basis function width of the RBF neural network is used to improve the prediction accuracy of the RBF network. Experimental results show that compared with Elman, BP, RBF and FA-RBF models, the constructed model has higher prediction accuracy, and the predicted value has higher fitting degree with the real value.

Key words: intelligent transportation system, traffic flow, short-term prediction, RBF neural network, improved firefly algorithm, chaotic search

中图分类号:

TP18

曹洁, 张敏, 张红, 陈作汉, 侯亮. 基于IFA优化RBF神经网络的短时交通流预测模型[J]. 兰州理工大学学报, 2022, 48(4): 99-104.

CAO Jie, ZHANG Min, ZHANG Hong, CHEN Zuo-han, HOU Liang. Short-term traffic flow prediction model based on IFA optimized RBF neural network[J]. Journal of Lanzhou University of Technology, 2022, 48(4): 99-104.

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基于IFA优化RBF神经网络的短时交通流预测模型

Short-term traffic flow prediction model based on IFA optimized RBF neural network

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