吉林大学学报(工学版) ›› 2014, Vol. 44 ›› Issue (3): 648-654.doi: 10.13229/j.cnki.jdxbgxb201403011

• 论文 • 上一篇    下一篇

模糊卡尔曼滤波在快速路行程时间估计中的应用

黄艳国1,2,许伦辉1,邝先验2   

  1. 1.华南理工大学 土木与交通学院,广州 510640;
    2.江西理工大学 电气工程与自动化学院,江西 赣州 341000
  • 收稿日期:2012-11-30 出版日期:2014-03-01 发布日期:2014-03-01
  • 通讯作者: 许伦辉(1965),男,教授,博士生导师.研究方向:智能交通控制,交通运输系统建模.E-mail:lhxu@scut.edu.cn E-mail:jxhuangyg@126.com
  • 作者简介:黄艳国(1973),男,副教授,博士研究生.研究方向:智能交通运输系统.E-mail:jxhuangyg@126.com
  • 基金资助:
    国家自然科学基金项目(61263024, 51268017);江西省自然科学基金项目(2010GQS0076);江西省教育厅科技项目(GJJ13428).

Application of fuzzy Kalman filter in travel time estimation in urban expressway

HUANG Yan-guo1,2, XU Lun-hui1, KUANG Xian-yan2   

  1. 1.School of Civil Engineering and Transportation, South China University of Technology, Guangzhou 510640, China;
    2.School of Electrical Engineering & Automation, Jiangxi University of Science & Technology, Ganzhou 341000, China
  • Received:2012-11-30 Online:2014-03-01 Published:2014-03-01

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摘要： 首先定义了新息的概念,通过在线监测新息的变化,将新息的均值和方差作为模糊控制器的输入,利用模糊逻辑对系统状态噪声和测量噪声的权重进行实时调整,建立了基于模糊逻辑的自适应卡尔曼滤波方法。克服了传统滤波器不能对环境变化进行实时跟踪的缺点,适应了交通状态的动态变化。通过用两种方法对广州市快速路段实测数据进行的对比分析发现,该方法与标准卡尔曼滤波相比具有良好的跟踪能力,在自由流状态和稳定流状态下,预测值与实测变化趋势一致,误差较小,拥挤状态相对误差基本维持在10%以下。

关键词: 交通运输系统工程, 城市快速路, 行程时间预测, 自适应卡尔曼滤波, 模糊逻辑

Abstract: First, the new information was defined and its change was online monitored. The mean and variance of new information were input into the fuzzy controller. Then, the fuzzy logic was used to adjust the importance weights of system noise and observation noise. The new method overcomes the shortcomings of traditional filter, which can not real-time track the change of environment. It adapts to the dynamic change of traffic state to realize the optimization estimation. The method was tested on urban expressway in Guangzhou using real-time detection data. The results show that the proposed method has better tracking ability than traditional Kalman filter. There was a slight difference between the prediction result and that of actual observation in free traffic flow state and stable flow, and the relative error was under 10% in congested traffic state.

Key words: engineering of communications and transportation system, urban expressway, travel time prediction, adaptive Kalam filter, fuzzy logic

中图分类号: 

  • U491.14
[1] Rice J, Van Zwet E. A simple and effective method for predicting travel times on freeways[J]. IEEE Transactions on Intelligent Transportation Systems, 2004, 5(3):200-207.
[2] 姜桂艳,常安德,张玮. 基于GPS浮动车的路段行程时间估计方法比较[J]. 吉林大学学报:工学版,2009,39(增刊2):182-186.
Jiang Gui-yan, Chang An-de, Zhang Wei. Comparison of link travel time estimation methods based on GPS equipped floating car[J]. Journal of Jilin University (Engineering and Technology Edition), 2009, 39(Sup.2): 182-186.
[3] Jeong R, Rilett L R. Prediction model of bus arrival time for real-time applications[J]. Transportation Research Record: Journal of the Transportation Research Board, 2005: 195-204.
[4] 王殿海,祁宏生,李志慧. 信号控制下的路段行程时间[J]. 吉林大学学报:工学版,2010,40(3):655-660.
Wang Dian-hai, Qi Hong-sheng, Li Zhi-hui. Road travel time under signal control[J]. Journal of Jilin University (Engineering and Technology Edition),2010,40(3):655-660.
[5] 张和生,张毅,胡东成. 路段平均行程时间估计方法[J]. 交通运输工程学报,2008, 8(1):89-96.
Zhang He-sheng, Zhang Yi, Hu Dong-cheng. Estimation method of average travel time for road sections[J]. Journal of Traffic and Transportation Engineering, 2008,8(1):89-96.
[6] Chien S I, Kuchipudi C M. Dynamic travel time prediction with real-time and historic data[J]. Journal of Transportation Engineering, 2003, 129(6): 608-616.
[7] 杨兆升,保丽霞,朱国华.基于Fuzzy回归的快速路行程时间预测模型研究[J]. 公路交通科技,2004,21(3): 78-81.
Yang Zhao-sheng, Bao Li-xia, Zhu Guo-hua. An urban express travel time prediction model based on fuzzy regression[J]. Journal of Highway and Transportation Research and Development, 2004,21(3): 78-81.
[8] Chowdhury N K, Deb Nath R P, Lee H, et al. Development of an effective travel time prediction method using modified moving average approach[J]. Lecture Notes in Computer Science, 2009, 5711: 130-138.
[9] Chang J, Chowdhury N K, Lee H. New travel time prediction algorithms for intelligent transportation systems[J]. Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology, 2010, 21(1): 5-7.
[10] Wu C H, Ho J M, Lee D T. Travel-time prediction with support vector regression[J]. IEEE Transactions on Intelligent Transportation Systems, 2004, 5(4): 276-281.
[11] 张娟,孙剑. 基于SVM 的城市快速路行程时间预测研究[J]. 交通运输系统工程与信息,2011, 11(2): 174-179.
Zhang Juan, Sun Jian. Prediction of urban expressway travel time based on SVM[J]. Journal of Transportation Systems Engineering and Information Technology, 2011, 11(2): 174-179.
[12] 徐天东,孙立军,郝媛. 城市快速路实时交通状态估计和行程时间预测[J]. 同济大学学报:自然科学版,2008, 36(10): 1355-1361.
Xu Tian-dong, Sun Li-jun, Hao Yuan. Real-time traffic state eEstimation and travel time prediction on urban expressway[J]. Journal of Tongji University(Nature Science), 2008, 36(10): 1355-1361.
[13] 温惠英,徐建闽,傅惠.基于灰色关联分析的路段行程时间卡尔曼滤波预测算法[J].华南理工大学学报:自然科学版,2006,34(9): 66-69.
Wen Hui-ying, Xu Jian-min, Fu Hui. Estimation algorithm with Kalman filtering for road travel time based on grey relation analysis[J]. Journal of South China University of Technology (Natural Science Edition), 2006, 34(9): 66-69.
[14] 马忠孝,刘宗玉,陈明. 基于模糊逻辑的自适应卡尔曼滤波在GPS/INS组合导航中的应用[J].信息与控制,2006, 35(4): 457-461.
Ma Zhong-xiao, Liu Zong-yu, Chen Ming. Application of adaptive Kalman filtering based on fuzzy logic to the integrated GPS/INS navigation[J]. Information and Control, 2006, 35(4):457-461.
[15] 严涛,王跃钢,杨波,等.模糊自适应卡尔曼滤波算法在航位推算系统中的应用[J].计算机测量与控制,2012, 20(3): 774-776.
Yan Tao, Wang Yue-gang, Yang Bo, et al. Application of fuzzy adaptive Kalman filter algorithm to dead reckoning system[J]. Computer Measurement & Control, 2012, 20(3): 774-776.
[16] Loebis D, Sutton R, Chudley J, et al. Adaptive tunning of a Kalman filter via fuzzy logic for an intelligent AUV navigation system[J]. Control Engineering Practice, 2004, 12(12): 1531-1539.
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