While there is no need for prior experience with Aimsun Next software to attend the beginners’ courses, we recommend that complete novices run through the tutorials in the Aimsun Next installer beforehand to familiarize themselves with the software.
Available courses languages are English, French and Spanish.
All in-person courses comprise 7 hours per day of instruction plus a 1-hour lunch break and two 15-minute coffee breaks. Standard start/finish time is 09:00 to 17:30. Hours differ for online courses.
We can supply official certificates upon course completion.
Registration fees depend on the course content and location. They include instruction, training materials and refreshments.
For the hands-on exercises, participants need to bring their own laptops, unless advised otherwise. We provide temporary licences of Aimsun Next software.
Registrations are subject to availability and payment must be received in full in advance of the course. We accept wire transfers and major credit cards.
Our beginners’ courses for Aimsun Next are offered in three modules, usually together in the same week.
The courses recommended vary based on your Aimsun Next edition.
Our most popular training course, this is a thorough introduction to the fundamentals of building a model in Aimsun Next, with a focus on simulation at the microscopic level.
This course will take you all the way through the workflow of a typical project, from editing to traffic management, scenarios, outputs and calibration. There are hands-on exercises every step of the way to make sure you have mastered the basic concepts and you should finish this course ready to start experimenting with Aimsun Next software.
Dynamic scenarios, experiments and replications
Attribute overrides and geometry configurations
Time series, thematic maps
Dynamic traffic assignment
Stochastic route choice algorithm and parameters
Dynamic user equilibrium algorithm and parameters
Microscopic network loading
Microscopic vehicle behavior
Calibration of a microscopic model
Traffic management actions
Modeling incidents and mitigation strategies
Modeling turn restrictions, reversible lanes, and other scheduled events
This course zooms out from microscopic simulation to look at larger scale modeling. You’ll learn about the event-based mesoscopic model and how to combine it with a more detailed time-sliced microsimulator.
Then you’ll get to experiment with static macroscopic modelling, which is often used as a preliminary step to dynamic modeling to extract the demand of a subarea from a regional model, to adjust and time-slice it, and to get an initial set of paths to warm-start dynamic assignment.
The Strategic Modeling course covers the traditional four-step modeling process of trip generation, trip distribution, mode choice, and trip assignment.
You should finish this course with an understanding of the role of travel forecasting within transportation planning and of the principles of each of the four modeling steps. You’ll be able to demonstrate how input data is used, and interpret the outputs from each step.
This training course guides you through the process of simulating pedestrians in an Aimsun Next microscopic simulation. You’ll learn the basics of the social force model, how to put together all the required pieces to run a pedestrian simulation and how to manage pedestrian flows interacting with other road users in a truly multimodal way.
Next, you’ll master handling conflicts between pedestrians and motorized vehicles at signalized as well as unsignalized intersections. You’ll also learn how to make pedestrians use public transportation and how to model their choice between walking and taking public transportation. Finally, you will evaluate the pedestrian simulation, examine its different applications and learn how to extend the mobility modeling workflow (for example the possibility of using Dynamic Public Transport Assignment to get skims for the four-step model).
It’s important to note that the pedestrian model embedded in Aimsun Next is designed to increase the realism of urban studies modeling multimodal networks, so this training course is primarily aimed at practitioners and engineers with a strong focus on road networks. For modeling high-fidelity pedestrian flows inside terminals and other buildings, you’ll need to use Aimsun Next in conjunction with third-party tools, such as Legion’s pedestrian simulator.
These are more advanced courses, designed for modelers who already have some practical experience.
Scripting in Aimsun Next is a powerful means of modifying a model, importing or exporting data and calculating and displaying results. This course teaches you how Aimsun Next scripts can dramatically increase your efficiency, including a full day of hands-on exercises.
Note that while the course includes a review of the Python language, you will definitely need a decent level of familiarity with Python and the concepts of programming.
Introduction to Python
Variables & data types
Control flow statements
Defining a function
Introduction to scripting in Aimsun Next
Creating a script
Executing a script
Using the scripting documentation
Architecture of the Aimsun platform
Aimsun Next scripting in practice – Exercises
Editing the model
Running a simulation
Customizing cost functions
If you are ready to start building more complex models that are on a larger scale and/or that integrate micro, meso and macro levels, then this is the course for you.
We’ll take you on a detailed walk-through of the methodology and workflow for complex projects with exhaustive tuition on calibration techniques, covering behavior parameters and route calculation, as well as real data comparison techniques.
Macroscopic, mesoscopic and microscopic modeling approach
Static and dynamic cost functions
Path calculation techniques
Multi-resolution modeling project workflow
Advanced calibration of mesoscopic and microscopic models
Procedures to estimate calibration parameters from real data
Tips and techniques to troubleshoot calibration issues
Importing real data
Statistic indicators to compare simulation outputs with real data