ArticleScientific reports2020
Networks and long-range mobility in cities: A study of more than one billion taxi trips in New York City.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
What it found
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Who cites it
12 citing papers in PubMed.
- Exploring the interplay between population profile and optimal routes in U.S. cities.Scientific reports · 2025Article
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- Research on urban economic centrality in the perspectives of knowledge stocks and flows.Heliyon · 2024Article
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- Intermunicipal travel networks of Mexico during the COVID-19 pandemic.Scientific reports · 2023Article
- Temporal visitation patterns of points of interest in cities on a planetary scale: a network science and machine learning approach.Scientific reports · 2023Article
- Exploring the mobility in the Madrid Community.Scientific reports · 2023Article
- A long-term travel delay measurement study based on multi-modal human mobility data.Scientific reports · 2022Article
- A multi-sensory stimulating attention model for cities' taxi service demand prediction.Scientific reports · 2022Article
- Activity of vehicles in the bus rapid transit system Metrobús in Mexico City.Scientific reports · 2022Article
- Modelling virus spreading in ride-pooling networks.Scientific reports · 2021Article
- Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We analyze the massive data set of more than one billion taxi trips in New York City, from January 2009 to December 2015. With these records of seven years, we generate an origin-destination matrix that has information of a vast number of trips. The mobility and flow of taxis can be described as a directed weighted network that connects different zones of high demand for taxis. This network has in and out degrees that follow a stretched exponential and a power law with an exponential cutoff distributions, respectively. Using the origin-destination matrix, we obtain a rank, called "OD rank", analogous to the page rank of Google, that gives the more relevant places in New York City in terms of taxi trips. We introduced a model that captures the local and global dynamics that agrees with the data. Considering the taxi trips as a proxy of human mobility in cities, it might be possible that the long-range mobility found for New York City would be a general feature in other large cities around the world.
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Registered trials
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