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.

Highlights

  • We analyze the massive data set of more than one billion taxi trips in New York City, from January 2009 to December 2015

  • We explore taxi trip records taking into account the administrative boundaries including the five boroughs of New York City[36]

  • Kennedy (JFK) International Airport and how by exploring the origins of the trips we can detect some features of the street network in New York City

Read more

Summary

Introduction

We analyze the massive data set of more than one billion taxi trips in New York City, from January 2009 to December 2015. We have geographic coordinates for all the nodes and the respective distances between them; as a result, the system can be described as a spatial network[28] With all this information, available through the analysis of trip records, we study the spatial activity of taxis as a dynamical process in this particular structure. In the study of mobility, the resulting structure is a spatial network and all the positions of the nodes are important, for instance, to determine the distance between two zones This example shows the vast amount of information that is captured in the origin-destination matrix and its direct relation to a network, allowing us to use the full potential of network science to study mobility

Methods
Results
Conclusion
Full Text
Published version (Free)

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call