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12 páginas, 1 tabla, 3 figuras, Human mobility is becoming an accessible field of study, thanks to the progress and availability of tracking technologies as a common feature of smart phones. We describe an example of a scalable experiment exploiting these circumstances at a public, outdoor fair in Barcelona (Spain). Participants were tracked while wandering through an open space with activity stands attracting their attention. We develop a general modelling framework based on Langevin dynamics, which allows us to test the influence of two distinct types of ingredients on mobility: reactive or context-dependent factors, modelled by means of a force field generated by attraction points in a given spatial configuration and active or inherent factors, modelled from intrinsic movement patterns of the subjects. The additive and constructive framework model accounts for some observed features. Starting with the simplest model (purely random walkers) as a reference, we progressively introduce different ingredients such as persistence, memory and perceptual landscape, aiming to untangle active and reactive contributions and quantify their respective relevance. The proposed approach may help in anticipating the spatial distribution of citizens in alternative scenarios and in improving the design of public events based on a facts-based approach., The research leading to these results has received funding by Barcelona City Council (Spain), RecerCaixa (Spain) through grant Citizen Science: Research and Education (F.B., J.R.P., A.O., M.G.R. and J.P.), by Strep-EU LASAGNE Project under contract no. 318132 (A.D.G. and O.S.), by MINECO (Spain) through grant nos. FIS2013-47532-C3-2-P (J.P. and M.G.R.) and FIS2012-38266-C2-2 (A.D.G. and O.S.), by Generalitat de Catalunya (Spain) through grant no. 2014- SGR-608 (M.G.R., O.S., J.P. and O.S.) and 2012-ACDC-00066 (M.G.R., O.S., J.P. and O.S.), and by Fundación Espaola para la Ciencia y la Tecnología (FECYT, Spain) through the Barcelona Citizen Science Office project of the Barcelona Lab programme (J.P.). O.S. also acknowledges financial support from Generalitat de Catalunya (FI-programme) and the Spanish MINECO (FPU-programme).

Sharing rides could drastically improve the efficiency of car and taxi transportation. Unleashing such potential, however, requires understanding how urban parameters affect the fraction of individual trips that can be shared, a quantity that we call shareability. Using data on millions of taxi trips in New York City, San Francisco, Singapore, and Vienna, we compute the shareability curves for each city, and find that a natural rescaling collapses them onto a single, universal curve. We explain this scaling law theoretically with a simple model that predicts the potential for ride sharing in any city, using a few basic urban quantities and no adjustable parameters. Accurate extrapolations of this type will help planners, transportation companies, and society at large to shape a sustainable path for urban growth., National Science Foundation (U.S.) (DMS-1513179), National Science Foundation (U.S.) (CCF-1522054)

Understanding human mobility is of vital importance for urban planning, epidemiology, and many other fields that draw policies from the activities of humans in space. Despite the recent availability of large-scale data sets of GPS traces or mobile phone records capturing human mobility, typically only a subsample of the population of interest is represented, giving a possibly incomplete picture of the entire system under study. Methods to reliably extract mobility information from such reduced data and to assess their sampling biases are lacking. To that end, we analyzed a data set of millions of taxi movements in New York City. We first show that, once they are appropriately transformed, mobility patterns are highly stable over long time scales. Based on this observation, we develop a supersampling methodology to reliably extrapolate mobility records from a reduced sample based on an entropy maximization procedure, and we propose a number of network-based metrics to assess the accuracy of the predicted vehicle flows. Our approach provides a well founded way to exploit temporal patterns to save effort in recording mobility data, and opens the possibility to scale up data from limited records when information on the full system is required., EU-LASAGNE Project (Contract 318132 (STREP)), Spain. Ministerio de Economia y Competitividad (Grant FIS2012-38266-C02-02), Generalitat de Catalunya (FI Program 2014-SGR-00608), Enel Foundation, Audi Volkswagen, SENSEable City Lab Consortium