Can Tradable Mobility Credits be effective, as “digital tolls“, for interurban mobility management? How could DRT services be utilized in an interurban context to further enhance network efficiency?
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.
The power of combining AI techniques with simulation in real time for accurate predictions and effective response plans under any conditions.
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 what does it really mean?
What is the value of data analytics in the mobility sector? The usual starting place is to look at historical data to find recurrences and trends but if you have real-time data feeds, then you can go deeper
Can Tradable Mobility Credits be effective, as “digital tolls“, for interurban mobility management? How could DRT services be utilized in an interurban context to further enhance network efficiency?
The ACUMEN research project team are creating a dynamic, data-driven digital framework for advanced network management. Marcel Sala updates us on their progress.
The journey toward fully autonomous driving is an exciting one, with innovations making cars smarter, safer, and more efficient. The EU-funded research project, i4Driving, contributes to this journey by developing a cutting-edge platform designed to test Autonomous Driving Systems (ADS). Alkis Papadoulis presents the latest advancements from the i4Driving team.
Aimsun is part of a pilot project to evaluate options for safely integrating active mobility devices into Singapore’s transport ecosystem.
How automation is helping local transport authorities to reduce the maintenance costs of traffic management systems.
Antonio Pellicer, PhD, explains Aimsun’s role in the SYNCHROMODE research project, focusing on enhancing transport network management through data-driven and simulation-based solutions.
Athina Tympakianaki, PhD, explains the potential benefits to our cities of the CONDUCTOR HE urban transit research project.
Mónica DomÃnguez, a data scientist at Aimsun, explains the paradigm shift from individual to community-aware transport solutions and how this has guided development of cooperative transport management strategies in the FRONTIER research project.
In 2022, Europe witnessed over 20,000 road fatalities. The European Commission’s ‘vision zero’ aims to eliminate all road fatalities and serious injuries by 2050.
Aimsun Next has introduced an extension for autonomous vehicle controllers, enhancing the simulation of vehicle behavior in complex traffic scenarios.This blog reviews the extension for the autonomous vehicle controller developed for Aimsun Next within the EU-funded FRONTIER EU Project
How Aimsun contributes to the TANGENT Project by integrating data-driven and simulation-based solutions into different tools and technologies developed within the project, aiming to optimise multimodal transport operations
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.
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.
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.
How can we explicitly represent, simulate and evaluate within-day behavioural (demand) and operational (supply) dynamics in emerging multi-actor passenger and freight transport systems?
Road safety is a major concern for public administrations worldwide: severe injuries and fatalities are not only a human tragedy but also have huge social costs
Evaluating safety in a traffic simulation environment has always been a controversial research topic because vehicles in a simulation are programmed in such a way that they cannot potentially cause accidents.
The EU-funded SPINE project employs simulation and data-driven models to virtually assess the impact of new solutions for increasing public transport’s modal share and user satisfaction.
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Aimsun Next 24
Aimsun (2024). Aimsun Next 24 User’s Manual, Aimsun Next Version 24.0.0, Barcelona, Spain. Accessed on: April. 16, 2024. [Online].
Available: https://docs.aimsun.com/next/24.0.0/
Aimsun Next 24
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title = {Aimsun Next 24 User’s Manual},
author = {Aimsun},
edition = {Aimsun Next 24.0.0},
address = {Barcelona, Spain},
year = {2024. [Online]},
month = {Accessed on: Month, Day, Year},
url = {https://docs.aimsun.com/next/24.0.0},
}​​​​​​​​​​​​​​​
Aimsun Next 24
TY – COMP
T1 – Aimsun Next 24 User’s Manual
A1 – Aimsun
ET – Aimsun Next Version 24.0.0
Y1 – 2024
Y2 – Accessed on: Month, Day, Year
CY – Barcelona, Spain
PB – Aimsun
UR – [In software]. Available:
https://docs.aimsun.com/next/24.0.0/
