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.
在新兴的多角色 (multi-actor) 客运和货运系统中，我们如何明确地呈现、仿真和评价日内 (within-day) 行为（需求层面）和运营（供应层面）的动态？
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 phrase “digital twin” has become ubiquitous in the field of transportation, but it has been applied so undiscriminatingly to so many different concepts that it is hard to know what people mean by it.
Machine learning can assist traffic simulation systems and reduce computational effort by providing network-wide traffic state estimation under unseen conditions.
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?
OK, big data is not dead, but the AI community is already moving beyond it and towards a new AI paradigm.
Supervised learning can help us to train our model to predict the right mobility patterns.
Unsupervised learning is a great tool for data analysis, particularly for understanding how mobility has changed due to COVID-19.
Classification problems with highly unbalanced datasets, such as incident detection, pose the trade-off between true positive rate and false negative rate.
Mobility patterns are a cornerstone of mobility demand modelling; clustering helps us extract usable daily patterns from huge sets of traffic data.
Aimsun Live is the ONLY predictive traffic management tool that combines AI techniques with simulation in real time – Paolo Rinelli explains why this is essential for accurate predictions and effective response plans under any conditions.
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