Date: Monday 13 November 2023


Claudio Roncoli is an Associate Professor of Transportation Engineering at Aalto University, Finland. He completed his PhD degree in System monitoring and environmental risk management (2013) at the University of Genova, Italy. Before joining Aalto University, he was a research assistant at the University of Genova, Italy (2007-2013), a visiting research assistant at Imperial College London, UK (2011-2012), and a Postdoctoral Research Associate at Technical University of Crete, Greece (2013-2016).

His research interests include real-time traffic management, modelling, optimisation, and control of traffic systems with connected and automated vehicles, as well as smart mobility and intelligent transportation systems. He is the author of more than 80 papers published in international peer-reviewed journals, conference proceedings, and contributed books. Claudio has been involved in several national and international research projects, also as principal investigator. He is a Deputy Editor-in-Chief for the IET Intelligent Transport Systems journal, an Associate Editor for the IEEE Transactions on Intelligent Transportation Systems and for the Proceedings of the Institution of Civil Engineers – Transport, and an Editorial Advisory Board Member for Transportation Research Part C: Emerging Technologies. He is a member of the Transportation Research Board (TRB) Standing Committee on Vehicle-Highway Automation (part of the US National Academy). He regularly acts as a reviewer for high-impact journals in the field, including IEEE Transactions on Intelligent Transportation Systems, Transportation Research Part C, and Transportation Research Part B, and he is a Scientific Committee member of several major international conferences.


In this talk, we will address the enhancement of on-demand ridesharing and mobility-on-demand (MoD) systems with a focus on real-time traffic-responsive vehicle assignment strategies and user-centred pricing models. First, we will introduce a predictive modelling approach that, integrating dynamic traffic conditions into ridesharing vehicle assignments, reduces congestion impact, thus improving travel and waiting times. Furthermore, we challenge the common assumption in existing research that travellers always comply with the MoD platform pricing and allocation, introducing a framework that balances pricing fairness and personal preferences. This dual approach not only accounts for travellers' choice, but also demonstrates that service quality can coexist with increased operational efficiency and profitability. The presentation will provide insights into how these innovative strategies can lead to more efficient and equitable urban transportation networks.

For more information contact:

Dr Elnaz Irannezhad