In this paper a Generalized Least Square estimator for the simultaneous estimation of O-D matrix and equilibrium traffic assignment model parameters is presented. The problem is formulated as fixed-point model (equilibrium programming) assuming the congested network case. In the optimization step the variability of both O-D demand vector and the matrix of link choice probabilities is considered. We assume as input information a set of observable network data, such as link traffic counts and travel time, as well starting estimates of both O-D matrix and models parameters. Along the paper, the theoretical aspects of the proposed estimator, the solution algorithm as well as the results of numerical applications are discussed.

Calibration of Equilibrium Traffic Assignment Models and O-D Matrix by Network Aggregate Data

Leonardo Caggiani;Michele Ottomanelli
2011-01-01

Abstract

In this paper a Generalized Least Square estimator for the simultaneous estimation of O-D matrix and equilibrium traffic assignment model parameters is presented. The problem is formulated as fixed-point model (equilibrium programming) assuming the congested network case. In the optimization step the variability of both O-D demand vector and the matrix of link choice probabilities is considered. We assume as input information a set of observable network data, such as link traffic counts and travel time, as well starting estimates of both O-D matrix and models parameters. Along the paper, the theoretical aspects of the proposed estimator, the solution algorithm as well as the results of numerical applications are discussed.
Soft Computing in Industrial Applications
978-3-642-20504-0
Springer
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11589/10714
Citazioni
  • Scopus 0
  • ???jsp.display-item.citation.isi??? 0
social impact