The power of combining AI techniques with simulation in real time for accurate predictions and effective response plans under any conditions.

Mobility patterns are a cornerstone of mobility demand modelling; clustering helps us extract usable daily patterns from huge sets of traffic data.

Classification problems with highly unbalanced datasets, such as incident detection, pose the trade-off between true positive rate and false negative rate.

Unsupervised learning is a great tool for data analysis, particularly for understanding how mobility has changed due to COVID-19.

Supervised learning can help us to train our model to predict the right mobility patterns.

OK, big data is not dead, but the AI community is already moving beyond it and towards a new AI paradigm.

In the last post we discussed the current trends in Machine Learning (ML) and the need to go beyond big data. But how does this affect the fields of ITS and smart cities?

Machine learning can assist traffic simulation systems and reduce computational effort by providing network-wide traffic state estimation under unseen conditions.

The phrase “digital twin” has become ubiquitous in the field of transportation, but what does it really mean?

Unlike purely data-driven methods, a simulation-based approach enables prediction of traffic states under recurrent conditions but also to predict the impact of incidents or changes to traffic control plans.

The MultiDEPART team developed tools to plan, manage and monitor DRT solutions in Lisbon, Barcelona Metropolitan Area and Thessaloniki, targeting PTAs and facilitating the harmonization and scalability of DRT services across European cities.

In this new article, Mark Brackstone takes a look at how this EU-funded research group has taken cost-benefit analysis to the next level.

When Main Roads Western Australia installed a new traffic management system, their staff needed to learn how to use it in a realistic but risk-free environment – simulation training was the perfect solution.

how the IMHOTEP research project is using simulation technology to improve airport management and ease the many pain points for customers traveling to and from busy airports.