基于改进灰狼算法优化BP 神经网络的短时交通流预测模型

交通运输系统工程与信息 ›› 2020, Vol. 20 ›› Issue (2): 196-203.

基于改进灰狼算法优化BP 神经网络的短时交通流预测模型

张文胜^{1, 2}，郝孜奇¹，朱冀军³，杜甜添^*4，郝会民⁵

1. 石家庄铁道大学交通运输学院，石家庄 050043；2. 河北省交通安全与控制重点实验室，石家庄 050043；3. 河北省交通规划设计院，石家庄 050011；4.天津轨道交通运营集团有限公司，天津 300222；5. 石家庄市勘察测绘设计研究院，石家庄 050011

收稿日期:2019-11-14 修回日期:2019-12-19 出版日期:2020-04-25 发布日期:2020-04-30
作者简介:张文胜(1971-)，男，宁夏隆德人，教授，博士.
基金资助:
河北省科技计划重点项目/Science and Technology Plan Project of Hebei Province (18390324D)；石家庄市科技计划项目/ Science and Technology Project of Shijiazhuang(181130034A，191260411A).

BP Neural Network Model for Short-time Traffic Flow Forecasting Based on Transformed Grey Wolf Optimizer Algorithm

ZHANGWen-sheng^{1, 2}, HAO Zi-qi¹, ZHU Ji-jun³, DU Tian-tian⁴, HAO Hui-min⁵

1. School of Traffic and Transportation, Shijiazhuang Tiedao University, Shijiazhuang 050043, China; 2. Traffic Safety and Control Laboratory of Hebei Province, Shijiazhuang 050043, China; 3. Hebei Provincial Communications Planning and Design Institute, Shijiazhuang 050011, China; 4. Tianjin Rail Transit Operation Group Co. Ltd, Tianjin 300222, China; 5. Shijiazhuang Transportation Management Office, Shijiazhuang 050011, China

Received:2019-11-14 Revised:2019-12-19 Online:2020-04-25 Published:2020-04-30

摘要/Abstract

摘要：

准确的短时交通流预测是交通控制和交通诱导的依据. 提出一种基于改进灰狼算法(TGWO)优化BP 神经网络的短时交通流预测模型(TGWO-BP)，有效提高短时交通流预测精度. 针对标准灰狼算法(GWO)收敛速度慢，容易陷入局部极值的问题，提出一种自适应递减的收敛因子，使灰狼算法区分全局搜索和局部搜索；改进灰狼个体的位置更新公式，引入惯性权重，调节惯性权重大小使灰狼算法具有跳出局部极值的能力；对比分析TGWO-BP、GWOBP 、PSO-BP、BP这4 种短时交通流预测模型，结果显示，TGWO-BP的短时交通流预测模型误差为10.03%，达到较好的预测精度.

关键词: 智能交通, 短时交通流预测, 改进灰狼算法(TGWO), BP神经网络, 收敛因子, 惯性权重

Abstract:

Accurate short-time traffic flow forecasting is the basis of traffic control and traffic induction. In this paper, a short time traffic flow forecasting model (TGWO- BP) is proposed based on transformed grey wolf optimizer algorithm (TGWO) and BP neural network, which can effectively improve the accuracy of short- time traffic flow forecast. Firstly, due to the drawbacks that the standard gray wolf algorithm converges slowly and tends to fall into the local extremum, an adaptive decreasing convergence factor is proposed, so that the grey wolf algorithm can distinguish the global search from the local search. Secondly, the position renewal formula of the gray wolf individual is improved by introducing the inertial weight. By adjusting the size of the inertial weight, the grey wolf algorithm has the ability to jump out of the local extremum. Finally, four short- time traffic flow forecasting models of TGWO-BP, GWO-BP, PSO-BP and BP are constructed, and the results show that the error of the short-time traffic flow forecasting model of TGWO-BP is 10.03%, and the accuracy of the prediction is better.

Key words: intelligent transportation, short-time traffic flow forecast, transformed grey wolf optimizer algorithm (TGWO), BP neural network, convergence factor, inertial weight

中图分类号:

U491.1

张文胜，郝孜奇，朱冀军，杜甜添，郝会民. 基于改进灰狼算法优化BP 神经网络的短时交通流预测模型[J]. 交通运输系统工程与信息, 2020, 20(2): 196-203.

ZHANGWen-sheng, HAO Zi-qi, ZHU Ji-jun, DU Tian-tian, HAO Hui-min. BP Neural Network Model for Short-time Traffic Flow Forecasting Based on Transformed Grey Wolf Optimizer Algorithm[J]. Journal of Transportation Systems Engineering and Information Technology, 2020, 20(2): 196-203.

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链接本文: http://www.tseit.org.cn/CN/abstract/abstract20030.shtml

http://www.tseit.org.cn/CN/Y2020/V20/I2/196

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