Lean&Powerful

With DynusT, engineers and planners can estimate the evolution of system-wide traffic flow dynamics patterns resulted from individual drivers seeking the best routes to their destinations responding to changing network demand, supply, or control conditions.

Simple to Use

DynusT is Windows based software. It is aimed at integrating with travel demand and microscopic simulation models, supporting application areas in which realistic traffic dynamic representation is needed for a large regional or corridor network.

Open Source

The DynusT Open Source Project has been created to promote and support the advancement of modeling theories and techniques to assist transportation agencies and practitioners in improved modeling and transportation policy decision making through state-of-the-art tools.

Globally Used

DynusT have been used in many projects worldwide. If you are using DynusT in a project, the DynusT Open Source Project is always looking for those interested in contribution to the community by publishing DynusT projects on the website.

DynusT+
DynusT Plus offers content to help DynusT users more quickly learn DynusT. The contents include video tutorials, training materials and utilities. The video tutorials currently feature videos that assist with converting travel demand models to DynusT. The training materials will include dates in the future for training sessions in training users to become DynusT certified users and trainers. The utilities includes post-processing tools to assist users with analyzing results.
DynusTPlus
Methodology Behind it
The Anisotropic Mesoscopic Simulation (AMS) model departs from previous models in that it is a vehicle-based mesoscopic traffic simulation approach that explicitly considers the anisotropic property of traffic flow into the vehicle state update at each simulation step. The advantage of AMS is its ability to address a variety of uninterrupted flow conditions in a relatively simple, unified and computationally efficient manner. AMS operates on a node-arc network representation, which is generally more memory efficient and temporally scalable than cell-based models from the standpoint of storing network-related attributes.
The gap function vehicle-based (GFV) solution algorithm is a computationally efficient procedure for solving the simulation-based dynamic traffic assignment problem. In contrast to the method of successive averages (MSA) based approach, for each iteration and each origin-destination-departure time combination, the amount of vehicles to be updated with a new path depends on the relative gap function value - the current solution’s proximity to the dynamic user equilibrium (DUE) condition - to implement both gradient-like search direction and step size methods. Vehicles with longer travel time are prioritized to be selected for path update. The proposed approach allows for faster convergence, compared with the MSA-based approach since each origin-destination-departure time combination has an individual search direction and step size. When vehicles are loaded with previously solved baseline, the DUE solution for alternative scenario analysis, the solution appears to be more consistent than the MSA-based approach as the proposed algorithm avoids over adjustment of flow that are not significantly affected by the network change in the alternative scenario. This results in preservation of consistent and robust assignment results.
DynusT is the only simulation-based DTA model capable of performing mesoscopic simulation and assignment for large-scale, regional networks for long time periods (e.g. 24-hr or greater). The Method of Isochronal Vehicle Assignment (MIVA) is a temporal decomposition scheme large spatial- and temporal-scale dynamic traffic assignment. As the analysis period is divided into what is known as Epochs, the 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.

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