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Communication devices (mobile networks, social media platforms) are produced digital traces for their users either voluntarily or not. This type of collective data can give powerful indications on their effect on urban systems design and development. For understanding the collective human behavior of urban city, the modeling techniques could be used. In this study the most important feature of human mobility is considered, which is the radius of gyration . This parameter is used to measure how (far /frequent) the individuals are shift inside specific observed region.
An increasing amount of location-based service (LBS) data is being accumulated and helps to study urban dynamics and human mobility. GPS coordinates and other location indicators are normally low dimensional and only representing spatial proximity, t
Academic fields exhibit substantial levels of gender segregation. To date, most attempts to explain this persistent global phenomenon have relied on limited cross-sections of data from specific countries, fields, or career stages. Here we used a glob
As more and more robots are envisioned to cooperate with humans sharing the same space, it is desired for robots to be able to predict others trajectories to navigate in a safe and self-explanatory way. We propose a Convolutional Neural Network-based
Human Trajectory Prediction (HTP) has gained much momentum in the last years and many solutions have been proposed to solve it. Proper benchmarking being a key issue for comparing methods, this paper addresses the question of evaluating how complex i
The research objectives are exploring characteristics of human mobility patterns, subsequently modelling them mathematically depending on inter-event time and traveled distances parameters using CDRs (Call Detailed Records). The observations are obta