Daily commuting to New York City. The color scale represents the daily number of commuters arriving to New York City from the different counties of the New York State. The 5 boroughs of New York City are aggregated and represented in black. (A): US Census Bureau data. (B): Flickr traces. (C): Gravity law. No maximum distance threshold was considered in this figure for the Flickr traces. Visualization created with matplotlib

(Guest post by Sabine Louet)

Getting urban planning right is no mean feat. It requires understanding how and when people travel between different places. This knowledge, in turn, helps in dimensioning roads and motorways and in scaling the capacity of utilities, such as power grids or mobile phone towers. Now, physicists at the Institute for Scientific Interchange Foundation in Turin, Italy, have exploited the geolocalisation data from millions of users of the photo sharing site Flickr to show how it is possible to predict crowd movements. Mariano Beiró and colleagues have combined this data with existing theoretical models explaining the movement of people. In a study published in EPJ Data Science, they show that their approach can help improve predictions concerning the nature of travel of large crowds of people between two places.

Beiró and colleagues thus showed how predictions of how many people fly between airports, or commute between two counties, can be improved.

Previously, social scientists proposed some theoretical models to explain how the flows of people or goods between cities are related to population distribution, economic development or travel distance. Thanks to geolocalization, these last 50 years of static modeling can now be refined. In this study, the authors have thus combined them with actual digital crumbs left by social media users showing how people travel in the U.S. at different scales of distance.

To do so, they used a hybrid algorithm based on machine learning and capable of integrating patterns by processing available data to infer results when data is not available. Beiró and colleagues thus showed how predictions of how many people fly between airports, or commute between two counties, can be improved. Future research could extend these methods to real-time predictions of the collective flows of people, as data becomes more widely and more rapidly accessible. This will help avoid potential traffic congestion or resource shortages due to an emergency situation, for example.