Multimodal urban mobility and multilayer transport networks
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Abstract (arXiv)
Transportation networks, from bicycle paths to buses and railways, are the backbone of urban mobility. In large metropolitan areas, the integration of different transport modes has become crucial to guarantee the fast and sustainable flow of people. Using a network science approach, multimodal transport systems can be described as multilayer networks, where the networks associated to different transport modes are not considered in isolation, but as a set of interconnected layers. Despite the importance of multimodality in modern cities, a unified view of the topic is currently missing. Here, we provide a comprehensive overview of the emerging research areas of multilayer transport networks and multimodal urban mobility, focusing on contributions from the interdisciplinary fields of complex systems, urban data science, and science of cities. First, we present an introduction to the mathematical framework of multilayer networks. We apply it to survey models of multimodal infrastructures, as well as measures used for quantifying multimodality, and related empirical findings. We review modelling approaches and observational evidence in multimodal mobility and public transport system dynamics, focusing on integrated real-world mobility patterns, where individuals navigate urban systems using different transport modes. We then provide a survey of freely available datasets on multimodal infrastructure and mobility, and a list of open source tools for their analyses. Finally, we conclude with an outlook on open research questions and promising directions for future research.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). First posted on arXiv 3 Nov 2021.
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Citing papers per year: 2025: 3; 2026: 1.
4 papers.
- 2026Emergence of Fragility in LLM-based Social Networks: the Case of Moltbookcited by 2
- 2025Multiresolution Analysis and Statistical Thresholding on Dynamic Networkscited by 1
- 2025FloGAN: Scenario-Based Urban Mobility Flow Generation via Conditional GANs and Dynamic Region Decouplingcited by 1
- 2025MSGCN: Multiplex Spatial Graph Convolution Network for Interlayer Link Weight Predictioncited by 0
- 2026Emergence of Fragility in LLM-based Social Networks: the Case of Moltbook24 Mar 2026
- 2025FloGAN: Scenario-Based Urban Mobility Flow Generation via Conditional GANs and Dynamic Region Decoupling16 Jul 2025
- 2025Multiresolution Analysis and Statistical Thresholding on Dynamic Networks1 Jun 2025
- 2025MSGCN: Multiplex Spatial Graph Convolution Network for Interlayer Link Weight Prediction24 Apr 2025