Vida Vukašinović

Researchshow recent

Randomised Optimisation Algorithms Research Network (2023-2027)
Computer structures and systems (2019-2027)
Fleet and traffic management systems for conducting future cooperative mobility (2022-2025)
Intelligent Secure Trustable Things (2020-2023)
Uncertainty Treatment and OPtimisation In Aerospace Engineering (2017-2020)
Biomedical data fusion by nonnegative matrix tri-factorization (2017-2020)
Improving Applicability of Nature-Inspired Optimisation by Joining Theory and Practice (2016-2020)
Trans fatty acids (2016-2019)
Synergy for Smart Multi-Objective Optimisation (2016-2019)
Resource Efficient Food and dRink for the Entire Supply cHain (2015-2019)
ERA Chair for isotope techniques in food quality, safety and traceability (2014-2019)
Computer structures and systems (2015-2018)
Adaptive Cooperative Control in Urban (sub) Systems (2013-2016)
Mobile application and pocket-size scale for diabetic patients (2013-2014)

Appliedshow all

Kranj’s Resilient Ecosystem for Active Transformation, Innovation, and Visionary Engagement (2025-2026)
Urban Pioneers - Systemic Change Amid Livable Environments (2023-2025)
Implementing gender equality plans to unlock research potential of RPOs and RFOs in Europe (2021-2025)
Social Innovation for integrated health CARE of ageing population in ADRION Regions (2020-2023)

Fleet and traffic management systems for conducting future cooperative mobility

acronym: CONDUCTOR
type: HE
duration: 2022-2025
https://conductor-project.eu/

content:
The CONDUCTOR project’s main goal is to design, integrate and demonstrate advanced, high‐level traffic and fleet management that will allow efficient and globally optimal transport of passengers and goods, while ensuring seamless multi‐modality and interoperability. Using innovative dynamic balancing and priority‐based management of vehicles (automated and conventional) CONDUCTOR will build upon state-of-the-art fleet and traffic management solutions in the CCAM ecosystem and develop next generation simulation models and tools enabled by machine learning and data fusion, enhancing the capabilities of transport authorities and operators, allowing them to become “conductors” of future mobility networks. We will upgrade existing technologies to place autonomous vehicles at the centre of future cities, allowing heightened safety and flexible, responsive, centralized control able to conduct traffic and fleets at a high level. These innovations will lead to less urban traffic and congestion, lowered pollution, and a higher quality of life. Project innovations will be integrated into a common, open platform, and validated in three use cases, testing the interoperability of traffic management systems and integration of different transportation means for both people and goods. Use case UC1 integrates traffic management with inter-modality, UC2 tests demand-response transport, and UC3 urban logistics. In each use case and its demonstrations, simulations will be validated through real life data.