A Parameter Identification Scheme for Second-Order Highway Traffic Model Based on Differential Algebraic Methodology - Intelligent Computing for Sustainable Energy and Environment - page 85

A Parameter Identification Scheme
for Second-Order Highway Trac Model
Based on Differential Algebraic Methodology
Nan Li 1 , 2 and Guangzhou Zhao 1
1 College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China
2 Faculty of Electronic and Control Engineering, Liaoning Technical University,
Huludao 125105, China
{happyapple,zhaoz}@zju.edu.cn
Abstract. In this paper, we develop a fast on-line and parameter identi-
fication scheme for the second-order macroscopic trac flow model. The
proposed parameter identification scheme is devised in the framework
of the algebraic identification, with differential algebra and operational
calculus as major mathematical tools. Compared to conventional meth-
ods, the new identification scheme allows the parameters of second-order
macroscopic trac model, namely free speed and critical density, be es-
timated in an on-line and computationally ecient fashion are identified
by means of differential algebra and operational calculus. The simula-
tion example of a hypothetical scenario demonstrates these advantages
numerically.
Keywords: parameter identification, highway model, differential
algebra.
1 Introduction
Trac congestion on highways is becoming an increasingly severe problem in
many countries all over the world. An effective and practically feasible approach
to tackle the congestion problem is through the application of various trac
control measures. Many trac control strategies are employed extensively, e.g.,
ramp metering, variable speed limit and route guidance. Design and evaluation
of these strategies usually involve macroscopic trac flow models in different
ways [1][2].
Due to the high non-linearity and time-varying features of highway trac
flow, which are coupled with the uncertainties of measurements, the parame-
ters of macroscopic models are usually hard to know exactly and influenced by
exogenous conditions, such as climate conditions (snow, rain or frog) or trac
incident, infrastructure downgrade and so on [3].
Therefore, for the purpose of effective management and control of transporta-
tion system, parameter identification (calibration) of trac model calls for on-
line and fast algorithms, which can deal with the real-time variations of trac
dynamics and randomness of observations.