Dr Nora Molkenthin

Nora Molkenthin studied Physics and Mathematics at Berlin, Manchester and Cambridge before obtaining a PhD on Climate Networks at PIK and HU Berlin within the interdisciplinary graduate school "Sichtbarkeit und Sichtbarmachung" in 2014. She then worked on PostDoctoral projects at MPIDS in Göttingen as well as TU Darmstadt and TU Dresden covering topics from Protein Folding to Social Networks and On-demand Ride-sharing. From 2020 to 2024 she co-lead the working group "Dynamics, stability and resilience of complex, hybrid infrastructure networks" at PIK. Since 2025 she works in the "Cities" group.

- Mobility and on-demand Ride-sharing

- Formation and structure of complex networks
- Collective variables and Master-Equation formalism for networked phenomena
- Mean-Field theory on and of networks
- Social networks
- Protein folding

Recent Highlights:

Transdisciplinary review of Shared pooled mobility bringing together expertise from different areas:

• Creutzig & Schmaus et. al. Shared pooled mobility: expert review from nine disciplines and implications for an emerging transdisciplinary research agenda, 2024 ERL

Ride pooling:

Understanding the emergent properties of shared pooled mobility to explore its potential role in a low-carbon society.

• Molkenthin, Schroeder, Timme, Scaling laws of collective ride-sharing dynamics, 2020 PRL
• Manik, Molkenthin, Topology dependence of on-demand ride-sharing, 2020 Applied Network Science
• Zech, Molkenthin, Timme, Schroeder, Collective dynamics of capacity-constrained ride-pooling fleets, Scientific Reports 2022

Network MCMC:

Using Markow-Chain Monte-Carlo to understand the network ensembles arising from complex functional network measures.

• Pfeffer, Molkenthin, Hellmann, Ensemble analysis of complex network properties—an MCMC approach, NJP 2022
• Ansari et. al. Moving the epidemic tipping point through topologically targeted social distancing, 2021 European Physical Journal

Collective variables:

• Lücke et. al. Learning interpretable collective variables for spreading processes on networks, PRE 2024
• Lücke et.al. Large population limits of Markov processes on random networks, Stochastic Processes and their Applications 2023

For complete up-to-date list of my publications go to my Publications on Google Scholar.

I am always happy to supervise Bachelor and Master theses, related to my research. Please contact me if you would like more information. Currently, the following topics are on my mind, but I am also open for related ideas by students.

1) Driver-centric routing - What can on-demand ride-pooling learn from informal public transport?

On-demand ride-pooling uses a centrally controlled algorithm to distribute timed origin-destination pairs onto a fleet of flexible routes. It has been studied as a high-tech addition to the ride-hailing and public transport system, primarily in urban centers of the global north. However, informal public transport, using flexible routes without the central planning, is found around the world, achieving much higher pooling rates than its high-tech cousin. This project aims to model informal public transport as a local optimization of passenger numbers and compare its efficiency to centrally optimized on-demand ride pooling.

2) Does ride-pooling with vehicle changes gravitate towards a line service at high fleet sizes and loads? 

The routes generated by on-demand pooling algorithms have been found to display mixed periodic and unstructured behaviours, which have an interesting interpretation as co-existing fixed- and flexible route transport systems. However, the existing research has not considered the effect of systems allowing for vehicle changes as part of route planning. This thesis would simulate routes with changes and analyze their patterns on the network using recurrence analysis to find out if and when a transition to line-service like behaviour emerges and weather its co-existence with flexible elements persists in this scenario.

3) What is the best transportation strategy for heterogeneous demand

Cities and suburban areas tend to be characterized by heterogeneous demand patterns with stronger demand in the center and weaker demand in the outskirts. This leads to the question how to optimally link the sparser, less frequent outskirts to the denser, more frequent service in the center. The objective of this thesis is to model and analyze this problem to better understand the mechanisms at play and find strategies to effectively address it.