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Computation of Metropolitan-Scale, Quasi-Dynamic Traffic Assignment Models Using High Performance Computing

Preliminary Results using Quasi-Dynamic Traffic Assignment for the Los Angeles Region

Project Introduction:

Connectivity enabled by telecommunications systems has introduced the opportunity to implement active control of vehicle routing across connected fleets. Static traffic assignment, which is used to estimate traffic states, does not have a notion of time dynamics and would not be able to represent these complex dynamics. Additionally, traffic assignment models for largescale models require significant computational resources and often takes days to run. The purposes of this project are: 1) leverage High-Performance Computing (HPC) capabilities to introduce a new generation model, a quasi-dynamic traffic assignment model that runs in compute times of hours for a metropolitan area, 2) examine mode shift opportunities by incorporating multimodal travel cost optimization algorithms, and 3) examine the energy impact by optimizing for fuel/energy consumption. The energy optimization for user-equilibrium and system optimality will be compared to traditional travel-time-based optimization algorithms that are used in the traffic assignment models.