Transportation systems face unprecedented challenges due to rising congestion, users’ dissatisfaction, pollution, and health risks. Simultaneously, the advent of autonomous vehicles, big data, and sharing economies provide remarkable opportunities to tackle these challenges through novel mobility paradigms. Intermodal autonomous mobility on demand (AMoD) systems – fleets of robot-taxis servicing travel demand jointly with public transport – are a promising solution to achieve sustainable mobility, as they combine the high-efficiency of public transport with fine-grained services of robot-taxis. However, realizing such systems hinges crucially on developing innovative mathematical tools and algorithms for the joint operation of robot-taxis and public transport. Our research combines recent advances in learning theory and big data with classical approaches in optimal control, operations research and game theory.

One project of our research is the exploration of Autonomous Mobility-on-Demand (AMoD) systems integrated with public transportation from a systematic optimization perspective. The framework uses a multi-commodity network flow model and column generation algorithms to efficiently manage large passenger volumes and congested networks. The other project studies an exact algorithm for the second-best toll pricing in multi-stakeholder transportation systems, in which a central authority designs road pricing schemes to manage traffic congestion. The focus is on using tolls from game theory to influence customer choices and optimize system performance. Through a combination of optimal control, game theory, and operations research, we are committed to redefining the future of urban mobility, making it more sustainable, efficient, and responsive to the needs of modern cities.