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A Temporal Domain Decomposition Algorithmic Scheme for Large-Scale Dynamic Traffic Assignment
With emergent interest of Simulation-Based Dynamic Traffic Assignment (SBDTA) in the field of transportation network modeling, deployment of SBDTA models for traffic operations and transportation planning have increased significantly 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. This paper presents a temporal decomposition scheme for large spatial- and temporal-scale dynamic traffic assignment, in which the entire analysis period is divided into Epochs. Vehicle assignment is performed sequentially in each Epoch, thus improving the model scalability and confining 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 confirm the promising performance of the proposed algorithmic schemes.
A Temporal Domain Decomposition Algorithmic Scheme for Large-Scale Dynamic Traffic Assignment
With emergent interest of Simulation-Based Dynamic Traffic Assignment (SBDTA) in the field of transportation network modeling, deployment of SBDTA models for traffic operations and transportation planning have increased significantly 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. This paper presents a temporal decomposition scheme for large spatial- and temporal-scale dynamic traffic assignment, in which the entire analysis period is divided into Epochs. Vehicle assignment is performed sequentially in each Epoch, thus improving the model scalability and confining 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 confirm the promising performance of the proposed algorithmic schemes.
A Temporal Domain Decomposition Algorithmic Scheme for Large-Scale Dynamic Traffic Assignment
Eric J. Nava (author)
2012
Article (Journal)
Electronic Resource
Unknown
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