Data and main statistical features for pedestrian mobility through a citizen science project
Creators: name: Larroya, Ferran
name: Perello, Josep
Publisher: name: Zenodo
Year: 2022
Subjects: complex systems
human mobility
pedestrian mobility
computational social science
citizen science
citizen social science
complex sociotechnical systems
stochastic processes

description: I have presented my work on human mobility through a city science project in schools in Barcelona. In my speech, titled: "Data and main statistical features for pedestrian mobility through a citizen science project", I presented a very nice citizen science project to study mobility in the schools' surroundings, and showed how we actually use this very valuable data to study pedestrian mobility and obtain very interesting statistical patterns. My talk is part of the annual workshop organized by the Association for the Study of Complex Socio-technological Systems which this year has been held at the University Rovira &amp; Virgili (URV). We study the GPS data from 83 students’ pedestrian journeys to reach their schools. The work is part of Bee-Path, an interdisciplinary citizen science project which also involves education and learning activities in 10 schools of the Barcelona Metropolitan Area. The mean squared displacement (MSD) shows a super-diffusive regime, almost ballistic (fully oriented, with no dispersion and with constant velocity). The time series of each individual instantaneous velocity shows a random path with reversing fluctuations towards a certain normal velocity level and the probability density function of all instantaneous velocities is observed to be consistent with a Log-Normal distribution. Furthermore, the observed reversing fluctuations around a given velocity value can be analyzed in terms of the autocorrelation, which shows a double exponential decay. Given these mobility statistical patterns, the logarithmic Ornstein-Uhlenbeck process appears to be a good candidate for modeling the pedestrian movement in these particular origin-destination journeys. Further statistical patterns, such as the turning angle memory or the tortuosity of the trajectory can be studied, which may reveal information on how oriented and straightforward the movement is, hinting at possible difficulties of access to schools due to urban structure. Ideally, it would be interesting to be able to compare and differentiate the different urban districts where the schools belong on the basis of the statistical parameters.
description: {"references": ["Barbosa, H., Barthelemy, M., Ghoshal, G., James, C. R., Lenormand, M., Louail, T., ... &amp; Tomasini, M. (2018). Human mobility: Models and applications. Physics Reports, 734, 1-74.", "Gualandi, S., &amp; Toscani, G. (2019). Human behavior and lognormal distribution. A kinetic description. Mathematical Models and Methods in Applied Sciences, 29(04), 717-753.", "Masoliver, J., &amp; Perell\u00f3, J. (2006). Multiple time scales and the exponential Ornstein\u2013Uhlenbeck stochastic volatility model. Quantitative Finance, 6(5), 423-433.", "Perell\u00f3, J., Montero, M., Masoliver, J., Farmer, J. D., &amp; Geanakoplos, J. (2020). Statistical analysis and stochastic interest rate modeling for valuing the future with implications in climate change mitigation. Journal of Statistical Mechanics: Theory and Experiment, 2020(4), 043210.", "Borovkov, K., &amp; Decrouez, G. (2013). Ornstein--Uhlenbeck Type Processes with Heavy Distribution Tails. Theory of Probability &amp; Its Applications, 57(3), 396-418.", "Tang, C. Y., &amp; Chen, S. X. (2009). Parameter estimation and bias correction for diffusion processes. Journal of Econometrics, 149(1), 65-81.", "Jones, M. C., &amp; Pewsey, A. \"A family of symmetric distributions on the circle\". Journal of the American Statistical Association 100(472): 1422-1428 (2005)."]}