Aimsun_Blog

The ‘data fever’ of the last few decades has undeniably led to great advances in the field of machine learning and artificial intelligence. We’ve seen a paradigm shift from automating processes to extracting insights from data—usually very large amounts of data. The transport modeling sector has been no exception to this trend, and we have seen a marked shift to data-driven (and hybrid) solutions. Is the era of full automation closer than ever? Or on the contrary, we can use recent advances for better real-time and long-term decision making and planning?

To explore such an exchange of data, strategies and decisions is the main goal of the H2020 EU project FRONTIER. Nineteen partners, including Aimsun, jointly research and develop a platform that aims to bridge the gap between data processing and decision making in complex mobility scenarios involving different key stakeholders. Cooperation and communication in FRONTIER are based on factual key performance indicators derived from simulation, data analysis and prediction within what we know as the Autonomous Network and Traffic Management Engine (ANTME) platform.

Aimsun has carried out several R&D tasks both within the Aimsun Next simulation framework and the data science and machine learning field. We have closely cooperated with the technical University of Crete (TUC) in the integration of autonomous vehicle controllers, expanding functionalities and user interface of Aimsun Next. Aimsun’s Data Science division has carried out research on incident detection both individually and in cooperation with ICCS. We are also benchmarking forecasting components and working together with Eurecat who is in charge of the Big Data pipeline and supply prediction module. We have published several papers on AI (see references below), and Aimsun recently held a Tutorial session on incident detection in the IEEE ITS Bilbao in cooperation with the 4FRONT Cluster.

In a world of eight billion inhabitants, future mobility technologies need to adapt to handling vast data flows and enhancing collaboration across different actors – the paradigm must shift from individual to community-aware solutions. Consequently, the challenge has ceased to be only a technological data-centric one – the emphasis should now be on multidisciplinary teams using technological advances (including data and artificial intelligence, of course) to communicate efficiently and to look ahead towards a common goal. The FRONTIER consortium has a firm conviction that recent advances are key for the transport community to take a step forward towards sustainable and efficient transport infrastructures.

Papers published by Aimsun/with Aimsun participation within FRONTIER:

  • Torrent-Fontbona, Dominguez, M, Fernandez, J and J. Casas. 2023. “Towards Efficient Incident Detection in Real-time Traffic Management”. MFTS, Dresden
  • Gkioka, G, Dominguez, M., Tympakianaki, A, Mentzas G. 2023. “AI-Driven Real-time Incident Detection for Intelligent Transportations Systems”. ITSC, Madrid
  • Tutorial session:  https://2023.ieee-itsc.org/accepted-tutorials/

References for curious minds about data science and artificial intelligence:

 

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