[Paper Review] Multimodal urban mobility and multilayer transport networks

Luis Guillermo Natera Orozco, Laura Alessandretti+3 more|arXiv (Cornell University)|Nov 3, 2021

Transportation Planning and Optimization4 citations

TL;DR

This paper presents a comprehensive review of multimodal urban mobility through the lens of multilayer transport networks, integrating complex systems, urban data science, and the science of cities. It introduces a network science framework to model interconnected transport modes, surveys key metrics, datasets, and open-source tools, and identifies critical research gaps in shared mobility modeling and individual decision-making in urban transit systems.

ABSTRACT

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.

Motivation & Objective

• To provide a unified, interdisciplinary overview of multimodal urban mobility and multilayer transport networks, addressing a gap in current research.
• To map the mathematical and computational frameworks used to model interconnected transport modes in urban environments.
• To compile and evaluate freely available datasets and open-source tools for analyzing multimodal mobility and infrastructure.
• To identify open research questions in shared mobility modeling, individual mode choice behavior, and real-time system integration.
• To advocate for open data and collaborative tools to advance sustainable urban mobility research and policy.

Proposed method

• Applies multilayer network theory to model urban transport systems, where each transport mode (e.g., walking, cycling, rail) constitutes a distinct layer with its own nodes and links.
• Uses network metrics such as average degree ⟨k⟩, connectivity, and inter-layer coupling to quantify structural properties of multimodal networks.
• Reviews empirical studies and observational data on human mobility patterns across multiple transport modes in real cities like Manhattan.
• Surveys existing open-access datasets (e.g., OpenStreetMap) and open-source software tools (e.g., for multilayer network analysis) for urban mobility research.
• Integrates findings from complex systems and urban data science to model dynamic interactions between transport modes and user behavior.
• Proposes a framework for future research that combines high-resolution mobility data with advanced modeling techniques like deep learning to study decision-making in multimodal systems.

Experimental results

Research questions

• RQ1 How can multilayer network models effectively represent the structural and dynamic interplay of different urban transport modes?
• RQ2 What network metrics best quantify the performance and integration of multimodal urban mobility systems?
• RQ3 How do individual mobility decisions—such as mode switching—emerge from interactions across transport layers?
• RQ4 What are the key challenges in modeling shared mobility services (e.g., bike-sharing, ride-sourcing) within integrated multimodal frameworks?
• RQ5 How can open data and open-source tools accelerate research and policy development in sustainable urban mobility?

Key findings

• Multilayer network models reveal that urban transport systems like Manhattan’s exhibit distinct structural properties across layers, with pedestrian and bicycle networks showing higher spatial density than rail or car networks.
• The average degree ⟨k⟩ varies significantly across layers—e.g., pedestrian networks have higher connectivity than rail networks—indicating differing accessibility and coverage.
• Despite the availability of high-quality geospatial data, access to spatio-temporally fine-grained and anonymized mobility data remains limited, often restricted by commercial data silos.
• Open-source tools for multilayer network analysis are available but not yet optimized for multimodal urban transport applications, indicating a need for domain-specific software development.
• The integration of shared mobility services into multimodal systems remains underdeveloped in current models, representing a key research gap.
• High-resolution individual mobility traces offer strong potential for uncovering microscopic decision-making processes that shape macroscopic mobility flows in urban networks.