Superior and sensible traffic simulation

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.

Consistent and intuitive dynamic equilibrium assignment

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.

Computational efficiency and scalability