Work

Mobility modeling through mobile data: generating an optimized and open dataset respecting privacy

Year: 2020

Type: conference-paper

Abstract: Modeling and understanding people's mobility at a temporal and geographical space are very strict requirements for developing better strategies of urban public and private transportation systems as well as establishing improved business techniques. This work proposes a random-search based approach to instantiate statistical indicators through an improved mobility scenario which provides specific information about people attending one or several days for some events. Then, we recreate that scenario with virtual humans, proposing a synthetic and open dataset that matches the original statistical data. The results show the proposed approach is very efficient to model people's mobility, and the generated data has a low error rate compared to the original one.

Source: International Wireless Communications and Mobile Computing Conference (IWCMC)

DOI: 10.1109/iwcmc48107.2020.9148138

Authors: Heber H. Arcolezi, Jean-Franeois Couchot, Oumaya Baala, Jean-Michel Contet, Bechara Al Bouna, Xiaokui Xiao