CANCELLED: Harnessing AI for Data-Driven Decision Making in Urban Mobility: Challenges and Opportunities
Location: Room - Opal II
Urban mobility is experiencing a transformative evolution, with data analytics and artificial intelligence (AI) offering unprecedented opportunities to enhance transportation systems. This session delves into the practical and scientific challenges of applying AI to urban mobility. Participants will explore how digital twin technology and data analytics are currently used to manage crowds during city events, gaining insights into practitioners' key requirements and future needs. The session will also showcase cutting-edge research on multi-task AI, addressing issues like data sparsity and complex behavior modeling to improve both immediate crowd control and longer-term planning. Additionally, we will examine the role of AI in real-time traffic and mobility management, discussing how short-term predictive models can enhance demand-responsive transport systems and multi-modal traffic management. An interactive round-table discussion will engage attendees in addressing ethical considerations, privacy concerns, and the establishment of learning communities to foster collaboration between practitioners and researchers. Join us for this comprehensive session to collaboratively address challenges and unlock the opportunities presented by AI in shaping the future of urban mobility.
Presenters
Elif Arslan, TU Delft Elif is a PhD candidate in the Transport & Planning Department at the Faculty of Civil Engineering and Geosciences, TU Delft. Her research focuses on developing explainable solutions for ride-hailing systems, with an emphasis on long-term cost minimization. She works on explainable AI models for short-term passenger demand and optimization techniques for real-time vehicle routing in ride-hailing systems, driven by the predictions. |
Dr.Serge Hoogendoorn, Prof., TU Delft Professor Serge P. Hoogendoorn is a leading expert in traffic flow theory and transportation systems. With over 20 years of experience, he has significantly advanced the understanding of traffic dynamics, pedestrian movements, and multi-modal transport networks. His research focuses on developing innovative models and strategies for traffic management, utilizing data analytics and artificial intelligence to enhance urban mobility. Professor Hoogendoorn has led numerous research projects aimed at improving the efficiency and sustainability of transportation systems worldwide. A prolific author, he has published extensively in esteemed journals and conferences. He actively collaborates with governmental agencies and industry partners to implement practical solutions, effectively bridging theoretical research with real-world applications. |
Dr.Sascha Hoogendoorn-Lanser, Director MICD, Delft University of Technology Sascha Hoogendoorn-Lanser is director of the Mobility Innovation Centre Delft (MICD), part of TU Delft. MICD conducts research into new modalities, with a strong focus on behaviour. We can develop great technologies, but how will this be used in practice? With sophisticated measuring methods and analysis techniques, the MICD provides a good picture of traveller behaviour, and the impact of new mobility on sustainability, liveability, safety and equity. The MICD initiates, stimulates, and facilitates research in the field of mobility, data collection and data analysis. This is done in cooperation with scientists from TU Delft and other research institutes, municipalities, sector organisations and companies. Whether it is large-scale European research or a local survey, MICD brings the right partners together, provides advice, sets up a study or analyses results. |
Jeroen Steenbakkers, Argaleo Jeroen Steenbakkers is founder of the Dutch digital twin company Argaleo. With more than 15 years of experience in simulation, modelling and data science he now provides data-driven solutions for policy- and decision makers. Aiming to create accessible, safe and sustainable urban environments. |
Yanyan Xu, TU Delft Yanyan Xu is a Ph.D. candidate at DAIMoND lab, the transportation department at Delft University of Technology. Her research focuses on multi-modal data fusion and multi-task learning for mobility prediction and crowd management. She is funded by the European Union project TULIPS to develop data-driven methods and tools for a sustainable transport management approach. |