Urban road traffic condition pattern recognition based on support vector machine

Jiaotong Yunshu Xitong Gongcheng Yu Xinxi/Journal of Transportation Systems Engineering and Information Technology

Volumn 13, Issue 1, 2013, Pages 130-136

  Yu, Rong ^a   Wang, Guo Xiang ^a   Zheng, Ji Yuan ^b,c   Wang, Hai Yan ^b  

^a NANJING UNIVERSITY OF FINANCE AND ECONOMICS   (China)

^b Jiangsu Province Institute of Quality and Safety Engineering   (China)

^c SOUTHEAST UNIVERSITY   (China)

Author keywords

LiBSVM; Pattern recognition; Support vector machine(SVM); Traffic state; Urban road traffic

Indexed keywords

CLASSIFICATION ACCURACY; CLASSIFICATION RESULTS; CONDITION RECOGNITION; DATA NORMALIZATION; INTELLIGENT TRANSPORTATION SYSTEMS; THREE DIMENSIONAL SPACE; TRAFFIC CONDITIONS; TRAFFIC STATE;

INTELLIGENT SYSTEMS; PATTERN RECOGNITION;

SUPPORT VECTOR MACHINES;

EID: 84875148086     PISSN: 10096744     EISSN: None     Source Type: Journal    
DOI: 10.1016/s1570-6672(13)60097-5     Document Type: Article

References (19)

1. PI X L, YANG X G, SUN Y. Application research of traffic state classification method based on collected Information from loop detector[J]. East China Highway, 2006(1): 33-38.
2. Adel W Sadek, Michael J Demetsky, Brain L Smith. Case-based reasoning for real time traffic flow management [J]. Computer-Aided Civil and Infrastructure Engineering, 1999, 14: 347-356.
3. Francesco Palmieri, Ugo Fiore. A nonlinear recurrence-based approach to traffic classification [J]. Computer Networks, 2009, 53(6): 761-773.
4. Angelica Lozano, Giuseppe Manfredi, Luciano Nieddu. An algorithm for the recognition of levels of congestion in road traffic problems [J]. Matherematics and computers in simulation, 2009, 76(6): 1926-1934.
5. M. Montazeri-Gh, A Fotouhi. Traffic condition recognition using the k-means clustering method [J]. Transportation Research B: Mechanical Engineering, 2011, 18 (4): 930-937.
6. Mohamed Abdel-Aty, Anurag Pande. Identifying crash propensity using specific traffic speed conditions [J]. Journal of Safety Research, 2005, 36(1): 91-108.
7. Andrzej Ruta, Yongmin Li, Xiaohui Liu. Real-time traffic sign recognition from video by class-specific discriminative features [J]. Pattern Recognition, 2010, 43(1): 416-430.
8. U S Transportation Research Board. Highway capacity manual [M]. China Communications Press, 2007.
9. LI Q Q, GAO D Q, YANG B S. Urban road traffic status classification based on fuzzy support vector machines [J]. Journal of Jilin University (Engineering and Technology Edition), 2009, 39: 131-134.
10. LI Z H, SUN D, JIN X X, et al. Pattern-Based study on urban transportation system states and properties with fuzzy reasoning methods [J]. Journal of Transportation Systems Engineering and Information Technology, 2008, 8 (5): 83-87.
11. REN J T, OU X L, ZHANG Y, et al. Pattern recognition of traffic states [J]. Journal of Highway and Transportation Research and Development, 2003, 20(2): 63-67.
12. Kannal L. Patterns in pattern recognition: 1968-1974 [J]. IEEE Trans. on Information Theory, 1974, 20 (6).
13. LI Z M. Traffic engineering [M]. China Communications Press, 2000.
14. YANG S Y. Pattern recognition and intelligent computing-MATLAB technology realization [M]. Publishing House of Electronics Industry, 2011.
15. J P Marques de Sá, WU Y F. Pattern recognition-Principle, method and application [M]. Tsinghua University Press, 2002.
16. SUN J X. Modern pattern recognition [M]. Higher Education Press, 2008.
17. Richard O Duda, Peter E Hart, David G Stork, LI H D. Pattern classification [M]. Publishing House of Electronics Industry, China OTIC Press, 2010.
18. ZHANG X G. Pattern recognition (Third edition) [M]. Tsinghua University Press, 2010.
19. Chih-Wei Hsu, Chih-Chung Chang, Chih-Jen Lin. A practical guide to support vector classification [EB/OL]. http://www.csie.ntu.edu.tw/~cjlin.