DYNAMEQ 2024 introduces Connected Autonomous Vehicle (CAV) functionality for the traffic simulator, several class-based options to provide more flexibility and control for the simulation-based matrix adjustment procedure, new scenario creation functionality including importing from Aimsun files and an updated CUBE importer, as well as enhancements to path editing and validation, control plan generation, warm start DTA and traversal matrix calculation. 

DYNAMEQ 24.0.1 provides enhancements and maintenance for the Aimsun import procedure, trajectory export from the vehicle animation window, and API support for simulation-based matrix adjustment.

Highlights are summarized below.

Connected Autonomous Vehicles (CAVs)

CAVs can now be modelled as a new category of vehicle type which includes an additional attribute called Connected Response Time. This attribute defines the minimum headway between a CAV and its leading vehicle when the leading vehicle is another CAV. When the leading vehicle is not a CAV, the minimum headway is the standard response time defined for the CAV vehicle type. The animation image below shows examples of CAVs (in red) platooning with low headways.

Simulation-based matrix adjustment

Simulation-based matrix adjustment now includes several class-based options for controlling the count-based adjustments, and a new feature for adjusting aggregate volume splits across classes. Matrix adjustments can be run from a Python script using the DYNAMEQ API.

Class-based options

• Traffic counts may represent only a subset of the available vehicle classes.
• Matrix adjustments may be restricted to a subset of the vehicle classes represented by the traffic counts.

Adjusting aggregate volume splits

• Time dependent target splits may be specified for a subset of vehicle classes.
• The subset may include vehicle classes that are not counted and those that are counted but exempt from the count-based adjustments.

Import from Aimsun

A scenario can now be created from a set of files extracted from an Aimsun network. The files provide a detailed description of the network including basic network geometry and connectivity among network elements, detailed lane information, transit data, signal data, vehicle properties and demand matrices. The lane data provides key information that allows the importer to produce a highly detailed copy of the original network.