Planning and Optimization for multimodal transportation networks in Smart Cities

In the past decades the gathering of data for operations within cities has increased exponentially providing us with opportunities for global optimization of systems that have so far been considered only in isolation. We shall use Los Angeles as a practical example of a city with rich set of transportation modes and diverse commuting patterns, showing the main challenges that are faced when optimizing the transportation for millions of people around the city for the total cost and energy. The main research challenges lie in designing planning algorithms for multimodal path-finding and learning the user behavior, while the technical challenges lie in real-time monitoring, execution and dispatching of the plans to the users.