Markovian traffic equilibrium assignment based on network generalized extreme value model

Yuki Oyama, Yusuke Hara, Takashi Akamatsu

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

This study establishes Markovian traffic equilibrium assignment based on the network generalized extreme value (NGEV) model, which we call NGEV equilibrium assignment. The use of the NGEV model for route choice modeling has recently been proposed, and it enables capturing the path correlation without explicit path enumeration. However, the theoretical properties of the model in traffic assignment have yet to be investigated in the literature, which has limited the practical applicability of the NGEV model in the traffic assignment field. This study addresses the research gap by providing the theoretical developments necessary for the NGEV equilibrium assignment. We first show that the NGEV assignment can be formulated and solved under the same path algebra as the traditional Markovian traffic assignment models. Moreover, we present the equivalent optimization formulations to the NGEV equilibrium assignment. The formulations allow us to derive both primal and dual types of efficient solution algorithms. In particular, the dual algorithm is based on the accelerated gradient method that is for the first time applied in the traffic assignment. The numerical experiments showed the excellent convergence and complementary relationship of the proposed primal and dual algorithms.

Original languageEnglish
Pages (from-to)135-159
Number of pages25
JournalTransportation Research Part B: Methodological
Volume155
DOIs
Publication statusPublished - 2022 Jan

Keywords

  • Accelerated gradient method
  • Markovian traffic assignment
  • Network generalized extreme value model
  • Partial linearization method
  • Path algebra
  • Stochastic user equilibrium

ASJC Scopus subject areas

  • Civil and Structural Engineering
  • Transportation

Fingerprint

Dive into the research topics of 'Markovian traffic equilibrium assignment based on network generalized extreme value model'. Together they form a unique fingerprint.

Cite this