Multi-Dimensional Traffic Flow Time Series Analysis with Self-Organizing Maps

Tsinghua Science and Technology

Volumn 13, Issue 2, 2008, Pages 220-228

  Chen, Yudong ^a   Zhang, Yi ^a   Hu, Jianming ^a  

^a TSINGHUA UNIVERSITY   (China)

Author keywords

k nearest neighbor (kNN); multi dimensional time series; self organizing maps (SOM); traffic flow prediction

Indexed keywords

CLUSTER ANALYSIS; SELF ORGANIZING MAPS; TIME SERIES ANALYSIS;

K-NEAREST NEIGHBOR; MULTI-DIMENSIONAL TIME SERIES; TRAFFIC FLOW PREDICTION;

TRAFFIC CONGESTION;

EID: 41049084644     PISSN: 10070214     EISSN: None     Source Type: Journal    
DOI: 10.1016/S1007-0214(08)70036-1     Document Type: Article

References (15)

1. Liao T. Clustering of time series data-A survey. Pattern Recognition 38 11 (2005) 1857-1874
2. William B M. Modeling and forecasting vehicular traffic flow as a seasonal stochastic time series process. [Dissertation]. University of Virginia, Charlottesville, USA, 1999.
3. Zhang H., Zhang Y., Li Z., et al. Spatial-temporal traffic data analysis based on global data management using MAS. IEEE Transaction on Intelligent Transportation Sys. 5 4 (2004) 268-275
4. Kohonen T. Self-Organizing Maps (1995), Springer-Verlag, Berlin Heidelberg
5. Kohonen T. Self-Organizing Maps. 2nd ed. (1997), Springer-Verlag, Berlin Heidelberg
6. Bretherton D, Bodger M, Baber N. Scoot-The future. In: 12th International Conference on Road Transport Information and Control (RTIC 2004). London, UK, 2004.
7. Kohonen T. Self-organizing maps of massive databases. International Journal of Engineering Intelligent Systems for Electrical Engineering and Communications 9 4 (2001) 179-185
8. Tamayo P., Slonim D., Mesirov J., et al. Interpreting patterns of gene expression with self-organizing maps: Methods and application to hematopoietic differentiation. Proceedings of the National Academy of Sciences of The United States of america 96 (1999) 2907-2912
9. Cheslow M, Hatcher S G, Patel V M. An initial evaluation of alternative intelligent vehicle highway systems architectures. MITRE Report 92W0000063. Virginia: McLean, 1992.
10. Yu G Q, Zhang C S. Switching ARIMA model based forecasting for traffic flow. In: Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). Montreal, Canada, 2004.
11. Hu J M, Zong C G, Song J Y, et al. An applicable short-term traffic flow forecasting method based on chaotic theory. In: Proceedings of the 2003 IEEE International Conference on Intelligent Transportation Systems. Shanghai, China, 2003.
12. Sun S., Zhang C., Yu G., et al. Bayesian network methods for traffic flow forecasting with incomplete data. Lecture Notes in Artificial Intelligence 3201 (2004) 419-428
13. Chen Y D, Zhang Y, Hu J M. Pattern discovering of regional traffic status with self-organizing maps. In: Proceeding of the 9th International Conference on Intelligent Transportation Systems (ITSC2006). Toronto, Canada, 2006.
14. Jolliffe I. Principal Component Analysis (1986), Springer-Verlag, Berlin Heidelberg
15. Goeppert J., and Rosenstiel W. Continuous interpolating self-organizing map. Neural Processing Letters 5 3 (1997) 185-192