
Modelling transport demand.
Quetzal is an open-source solution offering a framework and algorithms for the development of your transport models.
Developed since 2016 by SYSTRA’s transport modelling experts, Quetzal brings an alternative to closed-source software. As an open-source Python library, it provides an answer to the downsides such software may cause: affordability, integrability and transparency.
We provide you with the services of a team of modellers-developers dedicated to the design of your models and the integration of your mobility data.
Types of model

Transit Assignment Models
Quick to develop and cost-efficient, they allow public transport network trips to be accurately reproduced. They are ideal to enhance an origin-destination survey and inform the restructuring of a bus network.
Multimodal urban models
Whether strategic or operational, they assess public transport or road projects. You will be able to simulate all mode combinations including new mobilities.


Interurban models
Designed for long distance trips, they allow you to model commuter departure time decision and transfers. These models provide accurate estimates for each period of the day.

Compare each solution and Inform your Choice
Our Digital Solutions have been created to solve our clients complex problems. Given our wide selection of digital solutions that are often used across many different projects, we have broken each of our solutions down in an easy to understand brochure so you can find out exactly what solution will best suit your needs.
Benefits
Using Quetzal brings the following benefits to our clients:
- Custom-made transport models without software constraints
- Design of automated tools for data processing offering better responsiveness during study and reduced production times
- Delivery of light and fast models even on large networks thanks to continuously updated algorithms and model-software integration only possible with an open source library .

Want to know more? Come and meet us!

Guillaume Paix
Transport Planner
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