Large-scale, high-fidelity dynamic traffic assignment: framework and real-world case studies

Abstract

We present a highly detailed, microscopic Dynamic Traffic Assignment (DTA) framework with sufficient fidelity to address emergent and future planning and operations applications. Congestion patterns are estimated at the lane level with explicit modeling of complex signal timing algorithms and their impacts on queues and spillbacks. A Geographic Information System (GIS) ensures the most accurate network representation. The flexible representation of travel demand at the resolution of individual vehicles facilitates the capturing of sufficient vehicle and driver classes, vehicle performance and driving behavior distributions, inter-vehicle interactions, and temporally fine trajectories. Model outputs are saved at any desired granularity, and may be used to assess entire distributions of performance metrics to support reliability studies. Applications of the framework include the study of connected vehicles, Intelligent Transportation Systems (ITS), advanced tolling systems, and emissions modeling, and safety analysis. The above features are implemented with unparalleled computational performance so that large-scale networks may be handled without the need for accuracy-running time tradeoffs. We describe four real-world projects that clearly demonstrate the advantages of the microscopic DTA in practice.