A Temporal Domain Decomposition Algorithmic Scheme for Larger-Scale Dynamic Traffic Assignment

This paper will discuss how, with the emergent interest of Simulation-Based Dynamic Traffic Assignment (SBDTA) in the field of transportation network modeling, the deployment of SBDTA models for traffic operations and transportation planning have significantly increased in recent years. In parallel, research and development of innovative approaches of the SBDTA model have enhanced the quality of both the assignment component, i.e., improvement of convergence quality of the Dynamic User Equilibrium (DUE) problem, and the traffic simulation element. However, computational requirement remains to be one of the great challenges for DTA implementations on large-scale networks with a long analysis period. A temporal decomposition scheme for large scale spatial- and temporal-scale dynamic traffic assignment is presented in this paper, in which the entire analysis period is divided into Epochs. Vehicle assignment is sequentially performed in each Epoch and this improves the model scalability and confines the peak run-time memory requirement regardless of the total analysis period. A proposed self-turning scheme adaptively searches for the run-time-optimal Epoch setting during iterations regardless of the characteristics of the modeled network. Extensive numerical experiments have been conducted that confirm the promising performance of the proposed algorithmic schemes.


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  • Accession Number: 01447554
  • Record Type: Publication
  • Files: TRIS
  • Created Date: Sep 27 2012 4:42PM