Ride-pooling extends ride-hailing services by combining multiple similar trips into the same vehicle. App-based ride-pooling services are a promising option for flexible, efficient, and more sustainable urban mobility. They theoretically offer the possibility to reduce both the number of cars on the road as well as the total vehicle kilometers traveled.

We study the collective dynamics of these services to understand the fundamental dynamics of the vehicle fleets and the structure of the routes of individual vehicles. Building on simplified numerical simulations and scaling arguments from statistical physics, we explain universal properties of the fleet dynamics and the efficiency of these services across a wide range of street networks. Based on these insights, we develop and test ideas to make these services more efficient and adaptive, for instance, by dynamically combing stops as well as trips.

Integrating multi-dimensional empirical data analysis and game-theoretical modeling approaches, we analyze the interactions, incentives, and decisions of the users, drivers, and service providers. For example, we describe and understand artificial price surges caused by the collective behavior of ride-hailing drivers and reveal fundamental mechanisms underlying a sharp transition towards acceptance of ride-pooling services.

Mobility infrastructure like bike paths or tram lines, flexible mobility options like on-demand ride-hailing services, and charging infrastructure for electric vehicles are critical to enabling sustainable mobility. However, in urban areas, insufficient infrastructure or flexibility may discourage cycling or the use of public transport. Similarly, sustainable mobility options are often lacking in rural areas, leaving private combustion engine cars as the only viable alternative.

We study the impact of the available infrastructure on the collective dynamics and the efficiency of sustainable mobility options. Modeling of long-range traffic of battery-electric vehicles reveals a new form of congestion emerging from drivers' attempts to avoid queues at charging stations. Building on insights into cyclists' route choices and algorithms from network science, we develop data-driven optimization approaches to suggest demand-aware bike path networks. Comparing the dynamics of ride-pooling services with the efficiency of traditional forms of public mobility, we study where on-demand mobility is feasible, under which conditions ride-pooling services become sustainable, and when line-based services are more efficient.

As the digitalization of mobility services continues, mobility as a whole becomes more networked. Digital routing apps are ubiquitous, computing the fastest routes on short- and long-range trips. Within a city, similar apps may suggest multi-modal trips combining different public transportation modes. New shared mobility services like bike-sharing or ride-pooling offer additional flexible mobility options.

While access to more information allows users to adjust to the current situation, the increased interactions between all participants may lead to unintended collective dynamics. We study under which conditions such detrimental effects emerge, how they influence the efficiency of different mobility services, and how to design services and systems to avoid these problems.

Using agent-based simulations and dynamical systems analysis, we investigate how access to routing information changes traffic flow and explain why it may cause congestion instead of preventing it. By developing abstract models capturing the defining features of different modes of transportation, we study the competition and the synergies arising from the fundamental collective interactions.

Where2Share - Ridepooling-Potential in Germany (BMDV mFund project, 07/23 - 12/24)

StaCyNet - Statistical physics of cycling infrastructure networks (DFG Research Grant, 06/22 - 05/25)

PrePara - Nonlinear dynamics of epidemic spreading in urban transport networks - impact of paratransit in a case study for Cape Town, South Africa (Volkswagen Foundation Initiative Corona Crisis and Beyond, 03/21 - 01/23)

Networked systems like power grids typically operate around a steady state. However, they are subject to various external perturbations. Changes in consumption patterns between day and night induce long-term oscillations in demand. Smaller-scale perturbations arise from power trading and control mechanisms. Finally, renewable sources or random outages may cause unpredictable fluctuations on arbitrary time scales.

We study the response dynamics of networked systems in fundamental theoretical models of linear and nonlinear response to reveal universal features of the dynamics across applications [1]. Together with data-driven analysis of the perturbations and fluctuations [2,3], we apply this understanding to power grids to model their response to fluctuating power input and demand.

With the transition to renewable sources, power generation relies on many smaller units and less reliable sources like wind or solar power. Changes in the power usage due to the transition to electric mobility mirror these changes on the consumption side. This spatially more distributed and temporally intermittent structure of generation and consumption provides significant challenges for the reliability and stability of future-compliant energy systems.

We analyze how the volatile, fluctuating, and distributed generation from renewable sources affects the operational stability of power grids. Building on fundamental mathematical modeling of the response dynamics in complex networks, we develop conceptual and computational tools to model and predict spatio-temporal response patterns as a function of the network structure and quantify the reliability of energy systems and individual units.

We apply these insights to answer questions like: Which fundamental principles underlie robust power generation and transmission networks? Which control mechanisms are most effective to prevent catastrophic failures? How can we economically extend an existing network to increase its robustness?

The transition to more sustainability influences all aspects of our daily lives, from electricity consumption over mobility to health and economic prosperity. Solutions for each individual aspect affect and often inhibit progress in other areas.

For example, a fast transition to electric mobility is desirable to reduce emissions. However, increased and more distributed electricity demand may enforce fossil fuel use and increase emissions. Moreover, the construction of electric vehicles and the required infrastructure may itself cause increased emissions and even completely counter the positive effects on time scales relevant to prevent significant global warming [1,2].

Integrating concepts from complex systems research and various application areas, we study the effects and interactions of different aspects to reveal and understand systemic, often counterintuitive effects. We aim to develop a fundamental understanding of the complex networked interactions to identify and demonstrate possibilities to simultaneously achieve multiple, seemingly conflicting goals towards a sustainable future.

SynCON - Synchronization and Collective Nonlinear Dynamics in Complexified Oscillator Networks (DFG Research Grant, 04/24 - 03/27)

ResiNet - Resilience of Power Grids against Perturbations (BMBF project, 03/24 - 12/24)

CoNDyNet2 - Collective Nonlinear Dynamics of Complex Power Grids (BMBF project, 01/19 - 04/22)