A method of rapidly identifying chaos characteristics of short-term traffic flow based on small-data-sets

Tiedao Xuebao/Journal of the China Railway Society

Volumn 28, Issue 2, 2006, Pages 63-66

  Jiang, Hai Feng ^a   Ma, Rui Jun ^b   Wei, Xue Ye ^a   Wen, Wei Gang ^a  

^a BEIJING JIAOTONG UNIVERSITY   (China)

^b Xinjiang Polytechnic College   (China)

Author keywords

Chaos; Lyapunov exponent; Poincare section; Short term traffic flow; Small data sets

Indexed keywords

ANALYSIS; CHAOS THEORY; CONTROL SYSTEMS; FORECASTING; TIME SERIES ANALYSIS; TRAFFIC SIGNALS;

CHAOTIC CHARACTERISTICS; INTELLIGENT TRANSPORTATION CONTROL SYSTEM (ITS); LYAPUNOV EXPONENT; POINCARE SECTION; REAL TIME; SAMPLING DATA; SHORT TERM TRAFFIC FLOW; SMALL DATA SETS;

TRAFFIC CONTROL;

EID: 33745637494     PISSN: 10018360     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (9)

1. Dendrinos D S. Traffic flow dynamics: a search for chaos[J]. Chaos Solutions and Fractals, 1994, 4(4): 605-617.
2. Low D J, Addison P S. Chaos in a car following model with a desired headway time[A]. In proceeding of the 30 ISATA Conference[C]. Florence: 1997. 175-182.
3. Michael T, Rosenstein, James J Collins, and Carlo J. De Luca. A practical method for calculating largest Lyapunov exponents from small data sets[R]. NeuroMuscular Research Center and Department of Biomedical Engineering, November 20, 1992.
4. Ulrick P, Werner L. TSTOOL: Tools for nonlinear data analysis[J]. NOLTA'98. Crans-Montana, Switzerland: sept. 1998.
5. Sato S, Sano M, Sawada Y. Practical methods of measuring the generalized dimension and the largest Lyapunov exponent in high dimensional chaotic systems[J]. Prog. Theory. Phys., 1987, 77(1).