[1] Xiong Wei, Sun Lu, Zhou Jie,. Spline-based multi-regime traffic stream models [J]. Journal of Southeast University (English Edition), 2010, 26 (1): 122-125. [doi:10.3969/j.issn.1003-7985.2010.01025]

Copy

Spline-based multi-regime traffic stream models()

Share：

Journal of Southeast University (English Edition)[ISSN:1003-7985/CN:32-1325/N]

Volumn:

26

Issue:

2010 1

Page:

122-125

Research Field:

Traffic and Transportation Engineering

Publishing date:

2010-03-30

Info

Title:

Spline-based multi-regime traffic stream models

Author(s):

Xiong Wei¹;  Sun Lu²;  Zhou Jie³

¹ Quality Control Division, Department of Transportation of Anhui Province, Hefei 230051, China
²School of Transportation, Southeast University, Nanjing 210096, China
³Department of Computer Science, Northern Illin

Keywords:

traffic stream;  cluster analysis;  spline regression;  optimization

PACS:

U491

DOI:

10.3969/j.issn.1003-7985.2010.01025

Abstract:

In order to develop optimal multi-regime traffic stream models, a new method that integrates cluster analysis and B-spline regression is presented. First, for identifying the proper number of regimes, the K-means and the fuzzy c-means methods are applied in cluster analysis to actual traffic data, which suggests that dividing the traffic flow into two or three clusters can best reflect intrinsic patterns of traffic flows. Such information is then taken as guidance in spline regression, thus significantly reducing the computational burden of estimating spline models. Spline regression is used to estimate the locations of knots and the coefficients of the model so that the global error can be minimized. Model analysis results demonstrate that the proposed spline models have better fitting and generalization capability than the conventional models. In addition, the new method is more flexible in terms of data fitting and can provide smoother traffic stream models.

References:

[1] May A D. Traffic flow fundamentals[M]. New Jersey: Prentice Hall, 1990.
[2] Kockelman K M. Modeling traffic’s flow-density relation: accommodation of multiple flow regimes and traveler types [J]. Transportation, 2001, 28(4): 363-374.
[3] Sun L, Zhou J. Developing multi-regime speed-density relationships using cluster analysis [J]. Transportation Research Record, 2005(1934): 64-71.
[4] Duda R O, Hart P E, Stork D G. Pattern classification [M]. 2nd ed. New York: John Wiley & Sons, Inc, 2001.
[5] Dierckx P. Curve and surface fitting with splines[M]. Oxford Science Publications, 1993.
[6] Texas Department of Transportation. Roadway network of San Antonio in TransGuide Program [EB/OL].(2006-12-31)[2009-06-20].http://www.transguide.dot.state.tx.us/.
[7] Rousseeuw P J. Silhouettes: a graphical aid to the interpretation and validation of cluster analysis [J]. Journal of Computational and Applied Mathematics, 1987, 20(1): 53-65.
[8] Hastie T, Tibshirani R, Friedman J. The elements of statistical learning [M]. New York: Springer, 2001.

Memo

Memo:

Biographies: Xiong Wei(1971—), male, senior engineer; Sun Lu(corresponding author), male, doctor, professor, sunl@cua.edu.
Foundation item: The US National Science Foundation(No.BCS-0527508).
Citation: Xiong Wei, Sun Lu, Zhou Jie. Spline-based multi-regime traffic stream models[J]. Journal of Southeast University(English Edition), 2010, 26(1): 122-125.

Common functions