ArticleScientific reports2019
Revealing latent characteristics of mobility networks with coarse-graining.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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Who cites it
6 citing papers in PubMed.
- Mobility networks in Greater Mexico City.Scientific data · 2024Article
- Exploring the mobility in the Madrid Community.Scientific reports · 2023Article
- Compound hazards: An examination of how hurricane protective actions could increase transmission risk of COVID-19.International journal of disaster risk reduction : IJDRR · 2021Article
- Percolation of heterogeneous flows uncovers the bottlenecks of infrastructure networks.Nature communications · 2021Article
- Disparate patterns of movements and visits to points of interest located in urban hotspots across US metropolitan cities during COVID-19.Royal Society open science · 2021Article
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Previous theoretical and data-driven studies on urban mobility uncovered the repeating patterns in individual and collective human behavior. This paper analyzes the travel demand characteristics of mobility networks through studying a coarse-grained representation of individual trips. Building on the idea of reducing the complexity of the mobility network, we investigate the preserved spatial and temporal information in a simplified representations of large-scale origin-destination matrices derived from more than 16 million taxi trip records from New York and Chicago. We reduce the numerous individual flows on the network into four major groups, to uncover latent collective mobility patterns in those cities. The new simplified representation of the origin-destination matrices leads to categorization of trips into distinctive flow types with specific temporal and spatial properties in each city under study. Collocation of the descriptive statistics of flow types within the two cities suggests the generalizability of the proposed approach. We extract an overall displacement metric from each of the major flows to analyze the evolution of their temporal attributes. The new representation of the demand network reveals insightful properties of the mobility system which could not have been identified from the original disaggregated representation.
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Registered trials
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