Human-Centric Multimodal Mobility Powered by AI, Sensors, and Community Co-Design

Building on JKU-ITS established research in sensor fusion, connected vehicles, human–machine interaction, and data-driven mobility systems, we intend to redefine worthwhile door-to-door travel through integrated multimodal systems. Using real-world testbeds (including the JKU research vehicle with sensors, simulation platforms, and human factors studies) and advanced methods (machine learning, behavior modeling, cooperative systems), we will collect and analyze fine-grained data on multimodal travel experiences. We aim to co-design with communities, businesses, and institutions mobility solutions that merge public transport, active modes, and shared services, overcoming infrastructure, institutional, and digital barriers. Our approach draws on our track record in cooperative vehicle interaction, energy consumption optimization, behavior inference, and simulation of mixed traffic systems. Expected outputs include design frameworks grounded in user insights, case studies of multimodal implementations, and guidelines for mobility hubs that enhance public space quality, resilience, and sustainable transport adoption.