CHAIR OF

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SCIENCE

Considering Complex Customer Preferences in Multimodal Travel Itineraries

Easier access for travelers to a variety of individual mobility services via mobility applications and the integration of innovative means of transport have led to an increase in multimodal travel behavior in recent years. In this context, the combination of different transportation services within a certain period of time (mostly one week), or in particular within one trip, is referred to as multimodal mobility. In this research project, optimization approaches are developed which take complex individual customer preferences into account when orchestrating travel itineraries. In particular, the identification of a diversified and adequate amount of different travel itineraries under consideration of individual customer preferences will be discussed.

Contact person: Thomas Horstmannshoff

Dynamic Fleet Management in Ride-Sharing Systems

Worldwide increasing congestion in urban traffic networks and the associated air pollution have led to a growing interest in innovative shared mobility solutions. Among these are on-demand ride-sharing systems, which promise to improve efficiency by bundling travelers on the way from their origin to their destination. This allows for lower fares compared to individual taxi services and enables a more convenient travel experience compared to the traditional local public transport through smaller transport cabins and direct trips.

Within this research project, various aspects of dynamic fleet management are investigated, crucial for a successful ride-sharing system. The aim is to gain a comprehensive understanding of the planning problems involved, as well as to develop high-performance solution approaches that meet real-world requirements.

Contact person: Jarmo Haferkamp

Opportunities for Machine Learning in Urban Logistics

There has been a paradigm-shift in urban logistic services in the last years; global interconnectedness, urbanization, ubiquitous information streams, and increased service-orientation raise the need for anticipatory real-time decision making. A striking example are logistic service providers: Service promises, like same-day or restaurant meal delivery, dial-a-ride, and emergency repair, force logistic service providers to anticipate future demand, adjust to real-time traffic information, or even incorporate unknown crowdsourced drivers to fulfill customer expectations. Data-driven, anticipatory approaches are required to overcome the challenges of such services. They promise to improve customer satisfaction through accurate predictions (e.g., via supervised learning), enhanced fleet control (e.g., via reinforcement learning), and identification of demand patterns and delivery scenarios (e.g., via unsupervised learning). Within this research project, we combine recent advances in machine learning with established methods from operations research to tackle present-day challenges in urban logistics.

Contact person: Florentin Hildebrandt