In 2017, the University of Arizona restated DynusT’s proprietary software status and licensed DynusT to Metropia Inc. with the goal of propelling software development and enhancing user support. Through the DynusT/DynuStudio V2.0 platform, agencies and planners can employ the most advanced modeling features and capabilities available to develop multi-resolution models that better account for operational analysis related to capacity constraints, non-recurring congestion, emerg

DynusT is the best DTA model in the marketplace because: DynusT uses the micro-like Anisotropic Mesoscopic Simulation (AMS) model to produce traffic flow dynamics similar to microscopic models, but at 1000 times the speed. DynusT software was implemented with high-performance computing in mind. It is the only DTA model that is able to model a mega region like SCAG (Southern California Association of Governments) which incorporates 80k+ links and 60M+ daily trips. DynusT could

The above map shows the sample footprints of DynusT as of 2016. More than 20 states and 50 models have been created by DynusT users. DynusT is the most widely used DTA model with more than 200 projects/publications by users worldwide due to its superior traffic simulation realism and computational efficiency. DynusT has been used in the following projects. Most of them are large regional networks with daily simulation assignments: Work zone management (UA, SHRP2 R11, Kittleso

DynusT (Dynamic Urban Systems for Transportation) is a simulation-based dynamic traffic assignment (DTA) model with a 15 years development history that can support engineers and planners to address emerging transportation planning and operation challenges. DynusT users can estimate the evolution of system-wide traffic flow dynamics patterns (resulting from individual drivers seeking the best routes to their destinations) and respond to changing network demand, supply, or cont

DynuStudio® is the latest graphical and data management system based on more than 10 years of development to fully support DynusT mesoscopic dynamic traffic assignment (DTA) model. With DynuStudio®, users can convert GIS based planning networks to DynusT, edit networks and input additional information, manage scenarios, run DynusT, and visualize DynusT network simulation results for debugging, analysis and presentation purposes. DynuStudio® is aimed at supporting mission crit

Generally, the input data for DynusT can be classified into four categories: Demand, Supply, Calibration, and Validation. Demand - the demand category data is primarily the time-vary origin-destination (O-D) matrices, that is generally put into an hourly interval (can be shortened to intervals in minutes). The trip roster data are typically for either scenario comparisons or for activity-based model (ABM) integration. In the case of scenario comparison, one may want to use th

See the above chart for details. DynusT/DynuStudio takes roughly 20Gb of data per scenario.

DynusT models various intersection signal controls, ramp meters, and dynamic message signs. Signals: Pre-timed, actuated, pre-timed coordination, stop signs, yields Ramp metering: Fixed, adaptive Dynamic message signs: Congestion warning, mandatory detour, speed advisory To speed up the model creation process, DynuStudio allows the user to apply the default parameters and settings for both pre-timed and actuated controls. The user can revise the phasing and timing information

The DynusT research team developed a a two-stage model to calibrate the time-varying origin-destination (OD) matrices. The first-stage is to estimate O-D trip rates by minimizing link demand deviation with a one-norm formulation approach, aiming to efficiently match the traffic demand on calibration links with the link demand from the field data. Then, the second-stage is formulated as a time-dependent, user equilibrium traffic assignment model to adjust departure time profi

DynusT vehicular traffic simulation is based on the Anisotropic Mesoscopic Simulation (AMS) invented by Professor Yi-Chang Chiu. The AMS model is micro-like because vehicles are assumed to be moving forward based on the density in the individual vehicles's look-ahead speed influence range (SIR). The vehicle will decrease its speed if the SIR density increases and vice versa. This is rather similar to the car-following concept adopted by all microscopic traffic simulation mode