A Pseudo Markov-Chain Model and Time-Elapsed Measures of Mobility from Collective Data
This provides a new framework for understanding human mobility from aggregated data, with potential applications in environmental and sustainability contexts.
The authors tackled the problem of analyzing time-elapsed mobility flows from aggregated collective trip data by developing a pseudo Markov-chain model and corresponding mobility measures, applying it to the NetMob 2024 Data Challenge data to obtain results consistent with known commuting patterns.
In this paper we develop a pseudo Markov-chain model to understand time-elapsed flows, over multiple intervals, from time and space aggregated collective inter-location trip data, given as a time-series. Building on the model, we develop measures of mobility that parallel those known for individual mobility data, such as the radius of gyration. We apply these measures to the NetMob 2024 Data Challenge data, and obtain interesting results that are consistent with published statistics and commuting patterns in cities. Besides building a new framework, we foresee applications of this approach to an improved understanding of human mobility in the context of environmental changes and sustainable development.