Seamless door-to-door air travel
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.
分类： AI部落格
Real-time simulation training for traffic managers
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.
Taking cost-benefit analysis to the next dimension
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.
Demand Responsive Transport: flexible tools for planning and operational solutions
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.
迈向基于智能体的模型：仿真多式联运交通系统
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？
Mobility pattern prediction
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.
When is a digital twin not a digital twin?
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.
Towards ML-assisted traffic simulation
Machine learning can assist traffic simulation systems and reduce computational effort by providing network-wide traffic state estimation under unseen conditions.
How will ITS contribute to machine learning?
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?
Is this the death of big data?
OK, big data is not dead, but the AI community is already moving beyond it and towards a new AI paradigm.
How to use random forests to predict mobility patterns
Supervised learning can help us to train our model to predict the right mobility patterns.
Understanding mobility using unsupervised learning
Unsupervised learning is a great tool for data analysis, particularly for understanding how mobility has changed due to COVID-19.
Metric selection for incident detection systems
Classification problems with highly unbalanced datasets, such as incident detection, pose the trade-off between true positive rate and false negative rate.
How to extract patterns from traffic data for better insights into mobility
Mobility patterns are a cornerstone of mobility demand modelling; clustering helps us extract usable daily patterns from huge sets of traffic data.
AI alone is not enough for real-time traffic management – you need simulation too
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.
