The release candidate for our new major version, Aimsun Next 22, is now ready to download from https://www.aimsun.com/aimsun/downloads.
This is a ‘sneak preview’ of the full release that will arrive later this year and includes most of the new features we’ve been working on recently.
In this update we’d like to highlight a few of the most important features for you, namely our new electric vehicle battery consumption model, microscopic free-flow acceleration model, a model for vehicles that keep to the side of the road, and a more accurate time-dependent shortest path calculation.
Our new Electric Vehicle (EV) Battery Consumption Model complements the existing Fuel Consumption Model; it enables you to measure and visualize the performance of electric vehicles’ energy consumption in simulations. The model depends on vehicle dynamics, taking into account the different processes involved in the operation of EVs, and it also considers the ambient temperature due to the accessory power required to heat (or cool) the vehicle cabin.
We have also implemented the MFC model, which is sensitive to the performance of different engine types; these two new models, in combination with our Fuel Consumption Model, make Aimsun Next 22 the platform of choice for the increasingly urgent fields of EVs and environmental impact analyses.
The comparative simulations below illustrate a fall in emissions as the percentage of EVs increases.
In microsimulations, you can activate the option to allow non-lane-based behavior, to instruct vehicles to stay close to the left or right side of the road, depending on their direction of travel. There is a new tick-box option in the Vehicle Type dialog named Keep to the Left or Right Side.
The instruction creates a tendency for vehicles to keep to the side but, depending on traffic conditions and interaction with other vehicles and network controls, some vehicles will occasionally ignore the setting to simulate more accurate behavior. Also, it does not apply when vehicles need to cross lanes to turn right or left.
Its most obvious use is for bicycles, but auto-rickshaws or any other type of vehicle can be instructed to behave in this way.
When running DUE experiments, there is a new Path Cost option alongside Instantaneous and Experienced costs: Time-Dependent costs. You can find this new option on the Dynamic Traffic Assignment tab of dynamic experiment dialogs.
The new option enables a time-dependent shortest-path (TDSP) calculation which finds the least-cost route from an origin to a destination while considering the fact that the cost for a vehicle to traverse a link changes over time.
To simulate this, TDSP uses the time interval in which the path uses each link. This depends on the departure interval (when a vehicle was first generated in the network) plus its accumulated travel time up to the link whose cost is currently being calculated.
TDSP is best used in networks where most trips take significantly longer than the route-choice interval to reach their destinations, and in which congestion changes significantly over time. Large-scale models that cover peak periods tend to meet these conditions. In such cases, TDSP calculates better paths than both Instantaneous and Experienced costs.
Go here for the complete list of features in the candidate release.
Software Update Subscription (SUS)
If you have a SUS valid from August 2021, you’re ready to use this version of Aimsun Next. To renew your SUS, write to us at firstname.lastname@example.org and we’ll be happy to help.
Your feedback is especially valuable for candidate releases, so if you have any technical issues with this new release, please let us know at email@example.com.
We wish you all the best with the release candidate for Aimsun Next 22.