Statistical physics of cycling infrastructure networks

• Applicant: Dr. Malte Schröder
• Subject Area: Statistical Physics, Nonlinear Dynamics, Complex Systems, Soft and Fluid Matter, Biological Physics
• Term: since 2022
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 493613373

Cycling is one of the most efficient and sustainable alternatives to driving. Yet, it only accounts for a small fraction of trips made, even in urban areas with a large fraction of short to medium-distance trips. Existing research and provisional improvements during the COVID-19 pandemic demonstrate the importance of the available infrastructure to enable convenient and safe travel and promote cycling. However, despite a large body of research studying the structure and evolution of urban street and mobility networks and demonstrating the potential of network science and statistical physics approaches, the structure and dynamics of bike path networks have not been analyzed in the same detail.We propose to fill this gap by combining the wide range of existing statistical physics and network science tools with insights into the route and mode choice behavior of cyclists as well as empirical data on the existing bike path infrastructure to understand the structure of bike path networks in relation to the unique route choice dynamics. Our main objective is to advance the theoretical foundations and understanding of bike path networks, linking fundamental features of the network structure to the unique routing behavior and different social and economic constraints.Building on a preliminary proof-of-concept, we plan to extend our dynamic inverse percolation framework by integrating different aspects of cyclists’ route choice preferences for direct, safe, and convenient travel in the design and quantitative evaluation of bike path networks. Comparing results from theoretical analysis and empirical data-driven modeling in different street network topologies and demand distributions, we will uncover universal features such as common length scales emerging from the route choice dynamics. With these insights, we aim to answer questions like: Which structural properties make bike path networks `bike-friendly'? What properties do existing bike path networks have, and how could they be extended to provide the largest benefit for cyclists? How do bike path networks in this context interact with public transport and the overall urban mobility system? With a successful project, we will develop a comprehensive framework supporting the theoretical and empirical analysis of bike path networks and the application of network theoretical insights into their structure to help integrate cycling as a major part of sustainable urban mobility.

• DFG Programme: Research Grants