US 11,943,681 B2
Human mobility measuring method
Alberto Hernando De Castro, Lausanne (CH); and David Mateo, Lausanne (CH)
Assigned to KIDO DYNAMICS SA, Lausanne (CH)
Appl. No. 17/288,521
Filed by KIDO DYNAMICS SA, Lausanne (CH)
PCT Filed Oct. 15, 2019, PCT No. PCT/EP2019/077997
§ 371(c)(1), (2) Date Apr. 24, 2021,
PCT Pub. No. WO2020/083713, PCT Pub. Date Apr. 30, 2020.
Claims priority of application No. 18202716.9 (EP), filed on Oct. 25, 2018.
Prior Publication US 2021/0392463 A1, Dec. 16, 2021
Int. Cl. H04W 4/00 (2018.01); H01Q 1/24 (2006.01); H04B 7/06 (2006.01); H04M 15/00 (2006.01); H04W 4/029 (2018.01)
CPC H04W 4/029 (2018.02) [H01Q 1/246 (2013.01); H04B 7/0634 (2013.01); H04B 7/0656 (2013.01); H04M 15/41 (2013.01)] 10 Claims
 
1. Human mobility measuring method wherein a set of cell sites and/or towers, each comprising an antenna, distributed in a certain region and operating for a certain period of time giving support to a certain number of devices, wherein at each site, metadata of every telecommunication events concurring at its coverage area along this period of time are collected, and all the data is centralized in a single or multiple set of CDR raw metadata, said method comprising
a structuring step where CDR raw metadata is filtered so as to identify at least one of a device identification, a cell site identification, a date, and a time,
a data frame generating and sorting step where the filtered CDR metadata is sorted by user, by date, and by time,
a projecting step where the filtered sorted CDR metadata of each device are projected into an occupancy grid comprising a location vs. time-bin matrix, where locations are defined by groups of sites or towers, and time is divided in bins of arbitrary size,
a probabilistic map generating step of a device's location in space and time,
a filtering step consisting in filtering devices and events that accurately represent human mobility from patterns that reflect errors, uncertainties, or patterns not related with real human mobility, and
a gap filling process allowing for a continuous localization of the device by extrapolating any trajectory in space and time of any device, wherein the gap filling process comprises:
a first step consisting in representing the events registered at CDR as vectors which components are the amplitude of probabilities of occupation for each location and time,
a second step of constructing a density matrix from the vectors set obtained in the first step,
a third step of diagonalizing the density matrix obtaining a basis of orthonormal vectors, assuming that basis are also the eigenvectors of an underlying pseudo-Hamiltonian,
a third step of obtaining, for any incomplete vector, the coefficients of this vector in the basis of the pseudo-Hamiltonian limited in the subspace where this vector is defined,
a fourth step of extrapolating these coefficients to the subspace where the vector is not defined, and
a fifth step of obtaining the probabilities of occupation as the square of the obtained amplitudes.