Traffic state prediction based on competence region

Liu Qing-Chao; Lu Jian; Chen Shu-Yan

doi:10.7498/aps.63.140504

Abstract

Traffic state prediction is a key basis of traffic flow guidance system and traffic information publishing system. This paper presents a new method of forecasting the traffic state of unban expressway based on competence region. This method can predict the traffic state grade of road based on the distance between the sample data and the traffic state cluster center by creating a competence region of neural network classifier. And this method can effectively integrate the temporal and spatial features together without considering the correlation between the different features, and thus it has a strong adaptability. The experimental results show that this traffic state prediction method can reduce the prediction error and improve the equality coefficients compared with the classical algorithms. The prediction method used in this paper is effective and accurate for forecasting traffic state based on the competence region.

Keywords:

Authors and contacts

1.
Jiangsu Key Laboratory of Urban ITS, Southeast University, Nanjing 210096, China;
2.
Jiangsu Province Collaboratire Innovation Center of Modern Urban Traffic Technologies, Nanjing 210096, China
Funds: Project supported by the National High Technology Research and Development Program of China (Grant No. 2011AA110302), the Fundamental Research Funds for the Central Universities of Ministry of Education of China (Grant No. CXZZ13_0119).

References

[1]	Wang X Y 2010 Ph. D. Dissertation (Changchun: Jilin University) (in Chinese) [王新颖 2010 博士学位论文(长春: 吉林大学)]
[2]	Zhang H B, Sun X D, He Y L 2014 Acta Phys. Sin. 63 040505 (in Chinese) [张洪宾, 孙小端, 贺玉龙 2014 63 040505]
[3]	Zhang Y M, Wu X J, Bai S L 2013 Acta Phys. Sin. 62 190509 (in Chinese) [张玉梅, 吴晓军, 白树林 2013 62 190509]
[4]	Ma Q L, Liu W N, Sun D H 2012 Acta Phys. Sin. 61 169501 (in Chinese) [马庆禄, 刘卫宁, 孙棣华 2012 61 169501]
[5]	Bi J, Guan W 2012 Chin. Phys. B 21 068901
[6]	Zhang Y, Guan W 2009 Acta Phys. Sin. 58 756 (in Chinese) [张勇, 关伟 2009 58 756]
[7]	Cong R, Liu S L, Ma R 2008 Acta Phys. Sin. 57 7487 (in Chinese) [从蕊, 刘树林, 马锐 2008 57 7487]
[8]	Qi C, Hou Z S 2012 Cont Theo. Applic 29 465 (in Chinese) [齐驰, 侯忠生 2012 控制理论与应用 29 465]
[9]	Yao Z S, Shao C F 2007 China J. High. Trans. 20 113 (in Chinese) [姚智胜, 邵春福 2007 中国公路学报 20 113]
[10]	Xie J, Wu W 2011 J. Tongji Univ. (Nature Science) 39 1297 (in Chinese) [谢军, 吴伟 2011 同济大学学报 (自然科学版) 39 1297]
[11]	Ma L C, Xu W L 2011 Cont. Deci. 26 789 (in Chinese) [马林才, 许玮珑 2011 控制与决策 26 789]
[12]	Zang L L, Jia L 2007 China J. High. Trans. 20 95 (in Chinese) [臧利林, 贾磊 2007 中国公路学报 20 95]
[13]	Shen G J, Wang X H, Kong X J 2011 Sys. Eng. Theor. Pract. 31 561 (in Chinese) [沈国江, 王啸虎, 孔祥杰 2011 系统工程理论与实践 31 561]
[14]	Ding H, Zhang W H, Zheng X Y 2012 China J. High Trans. 25 126 (in Chinese) [丁恒, 张卫华, 郑小燕 2012 中国公路学报 25 126]
[15]	Don H H, Jia L M 2010 J. Trans. Sys. Eng. Info. Tech. 10 112 (in Chinese) [董宏辉, 贾利民 2010 交通运输系统工程与信息 10 112]
[16]	Kuncheva L I 2000 Proceedings of 4th International Conference on Knowledge Based Intelligent Engineering Systems and Allied Technologies Brighton, UK, Aug 30-Sept 1, 2000 p185
[17]	Kuncheva L I 2002 IEEE Trans. Sys. M. and Cyber. Part B: Cyber. 2 146
[18]	Verikas A Lipnickas A, Malmqvist K 1999 Patt. Recog. Lett. 4 429

Cited By

[1]	Wang X Y 2010 Ph. D. Dissertation (Changchun: Jilin University) (in Chinese) [王新颖 2010 博士学位论文(长春: 吉林大学)]
[2]	Zhang H B, Sun X D, He Y L 2014 Acta Phys. Sin. 63 040505 (in Chinese) [张洪宾, 孙小端, 贺玉龙 2014 63 040505]
[3]	Zhang Y M, Wu X J, Bai S L 2013 Acta Phys. Sin. 62 190509 (in Chinese) [张玉梅, 吴晓军, 白树林 2013 62 190509]
[4]	Ma Q L, Liu W N, Sun D H 2012 Acta Phys. Sin. 61 169501 (in Chinese) [马庆禄, 刘卫宁, 孙棣华 2012 61 169501]
[5]	Bi J, Guan W 2012 Chin. Phys. B 21 068901
[6]	Zhang Y, Guan W 2009 Acta Phys. Sin. 58 756 (in Chinese) [张勇, 关伟 2009 58 756]
[7]	Cong R, Liu S L, Ma R 2008 Acta Phys. Sin. 57 7487 (in Chinese) [从蕊, 刘树林, 马锐 2008 57 7487]
[8]	Qi C, Hou Z S 2012 Cont Theo. Applic 29 465 (in Chinese) [齐驰, 侯忠生 2012 控制理论与应用 29 465]
[9]	Yao Z S, Shao C F 2007 China J. High. Trans. 20 113 (in Chinese) [姚智胜, 邵春福 2007 中国公路学报 20 113]
[10]	Xie J, Wu W 2011 J. Tongji Univ. (Nature Science) 39 1297 (in Chinese) [谢军, 吴伟 2011 同济大学学报 (自然科学版) 39 1297]
[11]	Ma L C, Xu W L 2011 Cont. Deci. 26 789 (in Chinese) [马林才, 许玮珑 2011 控制与决策 26 789]
[12]	Zang L L, Jia L 2007 China J. High. Trans. 20 95 (in Chinese) [臧利林, 贾磊 2007 中国公路学报 20 95]
[13]	Shen G J, Wang X H, Kong X J 2011 Sys. Eng. Theor. Pract. 31 561 (in Chinese) [沈国江, 王啸虎, 孔祥杰 2011 系统工程理论与实践 31 561]
[14]	Ding H, Zhang W H, Zheng X Y 2012 China J. High Trans. 25 126 (in Chinese) [丁恒, 张卫华, 郑小燕 2012 中国公路学报 25 126]
[15]	Don H H, Jia L M 2010 J. Trans. Sys. Eng. Info. Tech. 10 112 (in Chinese) [董宏辉, 贾利民 2010 交通运输系统工程与信息 10 112]
[16]	Kuncheva L I 2000 Proceedings of 4th International Conference on Knowledge Based Intelligent Engineering Systems and Allied Technologies Brighton, UK, Aug 30-Sept 1, 2000 p185
[17]	Kuncheva L I 2002 IEEE Trans. Sys. M. and Cyber. Part B: Cyber. 2 146
[18]	Verikas A Lipnickas A, Malmqvist K 1999 Patt. Recog. Lett. 4 429

[1]	Liu Qi, Li Pu, Kai Chao, Hu Chun-Qiang, Cai Qiang, Zhang Jian-Guo, Xu Bing-Jie. Short-time prediction of chaotic laser using time-delayed photonic reservoir computing. Acta Physica Sinica, 2021, 70(15): 154209. doi: 10.7498/aps.70.20210355
[2]	Tian Zhong-Da, Li Shu-Jiang, Wang Yan-Hong, Gao Xian-Wen. Chaotic characteristics analysis and prediction for short-term wind speed time series. Acta Physica Sinica, 2015, 64(3): 030506. doi: 10.7498/aps.64.030506
[3]	Song Chang-Xin, Ma Ke, Qin Chuan, Xiao Peng. Infrared image segmentation based on clustering combined with sparse coding and spatial constraints. Acta Physica Sinica, 2013, 62(4): 040702. doi: 10.7498/aps.62.040702
[4]	Zhang Yu-Mei, Wu Xiao-Jun, Bai Shu-Lin. Chaotic characteristic analysis for traffic flow series and DFPSOVF prediction model. Acta Physica Sinica, 2013, 62(19): 190509. doi: 10.7498/aps.62.190509
[5]	Chen Shao-Kuan, Wei Wei, Mao Bao-Hua, Guan Wei. Analysis on urban traffic status based on improved spatio-temporal Moran's I. Acta Physica Sinica, 2013, 62(14): 148901. doi: 10.7498/aps.62.148901
[6]	Mao Yuan, Guo Li-Xin, Din Hui-Fen, Liu Wei. A new method to predict the wind direction from HF radar Doppler spectrum. Acta Physica Sinica, 2012, 61(4): 044201. doi: 10.7498/aps.61.044201
[7]	Chen Di-Yi, Liu Ye, Ma Xiao-Yi. Parameter joint estimation of phase space reconstruction in chaotic time series based on radial basis function neural networks. Acta Physica Sinica, 2012, 61(10): 100501. doi: 10.7498/aps.61.100501
[8]	Zhang Wen-Zhuan, Long Wen, Jiao Jian-Jun. Parameter determination based on composite evolutionary algorithm for reconstructing phase-space in chaos time series. Acta Physica Sinica, 2012, 61(22): 220506. doi: 10.7498/aps.61.220506
[9]	Ma Qing-Lu, Liu Wei-Ning, Sun Di-Hua. Multi-parameter fusion applied to road traffic condition forecasting. Acta Physica Sinica, 2012, 61(16): 169501. doi: 10.7498/aps.61.169501
[10]	Li He, Yang Zhou, Zhang Yi-Min, Wen Bang-Chun. Methodology of estimating the embedding dimension in chaos time series based on the prediction performance of radial basis function neural networks. Acta Physica Sinica, 2011, 60(7): 070512. doi: 10.7498/aps.60.070512
[11]	Li Jun, Zhang You-Peng. Single-step and multiple-step prediction of chaotic time series using Gaussian process model. Acta Physica Sinica, 2011, 60(7): 070513. doi: 10.7498/aps.60.070513
[12]	Zhang Chun-Tao, Ma Qian-Li, Peng Hong. Chaotic time series prediction based on information entropy optimized parameters of phase space reconstruction. Acta Physica Sinica, 2010, 59(11): 7623-7629. doi: 10.7498/aps.59.7623
[13]	Ma Qian-Li, Zheng Qi-Lun, Peng Hong, Qin Jiang-Wei. Chaotic time series prediction based on fuzzy boundary modular neural networks. Acta Physica Sinica, 2009, 58(3): 1410-1419. doi: 10.7498/aps.58.1410
[14]	Wang Yong-Sheng, Sun Jin, Wang Chang-Jin, Fan Hong-Da. Prediction of the chaotic time series from parameter-varying systems using artificial neural networks. Acta Physica Sinica, 2008, 57(10): 6120-6131. doi: 10.7498/aps.57.6120
[15]	Yan Hua, Wei Ping, Xiao Xian-Ci. An adaptive approach based on Bernstein polynomial to predict chaotic time series. Acta Physica Sinica, 2007, 56(9): 5111-5118. doi: 10.7498/aps.56.5111
[16]	He Tao, Zhou Zheng-Ou. Prediction of chaotic time series based on fractal self-affinity. Acta Physica Sinica, 2007, 56(2): 693-700. doi: 10.7498/aps.56.693
[17]	. Prediction of chaotic time series based on selective support vector machine ensemble. Acta Physica Sinica, 2007, 56(12): 6820-6827. doi: 10.7498/aps.56.6820
[18]	Zhang Jun-Feng, Hu Shou-Song. Chaotic time series prediction based on RBF neural networks with a new clustering algorithm. Acta Physica Sinica, 2007, 56(2): 713-719. doi: 10.7498/aps.56.713
[19]	Ye Mei-Ying, Wang Xiao-Dong, Zhang Hao-Ran. Chaotic time series forecasting using online least squares support vector machine regression. Acta Physica Sinica, 2005, 54(6): 2568-2573. doi: 10.7498/aps.54.2568
[20]	Li Jun, Liu Jun-Hua. On the prediction of chaotic time series using a new generalized radial basis function neural networks. Acta Physica Sinica, 2005, 54(10): 4569-4577. doi: 10.7498/aps.54.4569

Catalog

Vol. 63, Issue 14 - 2014-07-20

Metrics

Abstract views: 5893
PDF Downloads: 581
Cited By: 0

姓名
邮箱
手机号码
标题
留言内容
验证码

Search

Article

留言板