计算机工程 ›› 2020, Vol. 46 ›› Issue (6): 34-39.doi: 10.19678/j.issn.1000-3428.0055520

• 热点与综述 • 上一篇    下一篇

暴雨天气下高速公路短时交通流预测

蔡延光1, 乐冰1, 蔡颢2, 李旭阳1   

  1. 1. 广东工业大学 自动化学院, 广州 510006;
    2. 奥尔堡大学 健康科学与工程系, 丹麦 奥尔堡 9220
  • 收稿日期:2019-07-19 修回日期:2019-09-04 发布日期:2019-09-23
  • 作者简介:蔡延光(1963-),男,教授、博士、博士生导师,主研方向为网络控制与优化、智能交通系统;乐冰,硕士;蔡颢(通信作者),博士;李旭阳,硕士。
  • 基金项目:
    国家自然科学基金(61074147);广东省自然科学基金(S2011010005059);广东省教育部产学研结合项目(2012B091000171,2011B090400460);广东省科技计划项目(2012B050600028,2014B010118004,2016A050502060);广州市科技计划项目(201604016055);广州市花都区科技计划项目(HD14ZD001);广州市天河区科技计划项目(2018CX005)。

Short-Term Traffic Flow Forecast of Expressway Under Heavy Rain

CAI Yanguang1, LE Bing1, CAI Hao2, LI Xuyang1   

  1. 1. School of Automation, Guangdong University of Technology, Guangzhou 510006, China;
    2. Department of Health Science and Technology, Aalborg University, Aalborg 9220, Denmark
  • Received:2019-07-19 Revised:2019-09-04 Published:2019-09-23

摘要: 在暴雨天气情况下,驾驶人视野受限制容易引发交通事故。为准确预测暴雨天气下的高速公路车流量从而减少事故的发生,提出一种基于改进布谷鸟搜索(CS)算法与径向基函数(RBF)神经网络的高速公路交通流预测方法。采用猴群算法中的猴爬山过程优化布谷鸟位置更新策略,通过识别概率自适应更新策略建立改进的CS-RBF神经网络(CS-RBFNN)交通流预测模型。实验结果表明,相对于改进的GSO-RBFNN模型,改进的CS-RBFNN模型具有更快的收敛速度和更高的预测精度,其平均绝对百分比误差为8.2%,平均绝对误差为20.14,均方根误差为19.2,且预测准确率高于90%。

关键词: 暴雨天气, 高速公路, 改进布谷鸟搜索算法, 神经网络, 交通流预测

Abstract: The incidence rate of traffic accidents increases in heavy rain due to the limited vision of drivers.In order to accurately predict the traffic flow of expressway under heavy rain and reduce accidents,this paper proposes a method based on the improved Cuckoo Search(CS) algorithm and Radial Basis Function(RBF) neural network for expressway traffic flow forecast under heavy rain.The method uses monkey climbing process in monkey swarm algorithm to optimize the cuckoo position update strategy,and then adopts the adaptive update strategy for recognition rate to establish a traffic flow forecast model for expressway based on improved CS-RBF Neural Network(CS-RBFNN).Experimental results show that the improved CS-RBFNN model has a higher convergence speed and prediction accuracy than the improved GSO-RBFNN model.The Mean Absolute Percentage Error(MAPE) of the proposed method is 8.2% and the Mean Absolute Error(MAE) is 20.14.The Root Mean Square Error(RMSE) of the method is 19.2,and its prediction accuracy is higher than 90%.

Key words: heavy rain, expressway, improved Cuckoo Search(CS) algorithm, neural network, traffic flow forecast

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