BAMBOO

BAMBOO will develop new methodologies and algorithms to extract travel demand information from anonymised mobile phone records and to integrate such information into state-of-the-art transport simulation systems and interactive visualisation tools.

HARMONY

Holistic Approach for Providing Spatial & Transport Planning Tools and Evidence to Metropolitan and Regional Authorities to Lead a Sustainable Transition to a New Mobility Era

MOMENTUM

Modelling Emerging Transport Solutions for Urban Mobility

IMHOTEP

Integrated Multimodal Airport Operations for Efficient Passenger Flow Management

FRONTIER

Next generation traffic management for empowering CAVs integration, cross-stakeholders collaboration and proactive multi-modal network optimization

LAMBDA-V

One-year feasibility study processing existing datasets, to understand the parameters needed for modelling human drivers and how to extend them to make vehicle rules for CAVs

HumanDrive

To enable the completion of a 230 mile journey across the UK, setting a precedent in the UK for the successful deployment of an autonomous, human-like vehicle undergoing a complex journey through real-world driving conditions.

Vericav

To significantly improve test efficiency when evaluating multiple driving scenarios, simultaneously replicating the behaviour and actions when faced with obstacles in a realistic and consistent manner.

Levitate

Development and application of Aimsun to the assessment of autonomous and connected vehicles.

OmniCAV

Aimsun was expected to provide the project’s core traffic simulation environment, used to identify areas where testing in the real world is necessary.

Is this the death of big data?

The era of big data is ending. This might seem like a crazy thing to say at a time when machine learning, deep learning and data science are being integrated in ever more businesses and institutions. But the truth is that the AI community is already moving beyond big data.

How to generate throughput and demand outputs

September 2021: In traffic modelling, it can be useful to compare the traffic demand and the throughput along a route: how many people would like to make a journey and how many were able to carry out that journey within a set time frame.

Why companies should invest in research

Mark Brackstone’s LinkedIn, Sep 2021: Did you know that Aimsun now has a dedicated Research Projects team? Created in January this year, our new team already has three members with more to join us soon.

How to use random forests to predict mobility patterns

In the previous article, we extracted a set of nine flow patterns from a two-year dataset (2018-2019). However, now there is no direct mapping between the day of the week and patterns. The rules for assigning a pattern to each day are more complicated. Obviously, if we have the measured flow of each day, we can just calculate the distance between each day and each pattern and there we have it. However, the true value lies in doing it in advance, without the measured flow. We can use all the work that we saved in using clustering to extract patterns when we need to predict how to assign a pattern.

Aimsun Next 22 Release Candidate is ready

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.