Human Mobility in a Continuum Approach

Abstract

Human mobility is investigated using a continuum approach that allows to calculate the probability to observe a trip to any arbitrary region, and the fluxes between any two regions. The considered description offers a general and unified framework, in which previously proposed mobility models like the gravity model, the intervening opportunities model, and the recently introduced radiation model are naturally resulting as special cases. A new form of radiation model is derived and its validity is investigated using observational data offered by commuting trips obtained from the United States census data set, and the mobility fluxes extracted from mobile phone data collected in a western European country. The new modeling paradigm offered by this description suggests that the complex topological features observed in large mobility and transportation networks may be the result of a simple stochastic process taking place on an inhomogeneous landscape.

How Big Data Fast Tracked Human Mobility Research and the Lessons for Animal Movement Ecology

“…Instead, it does make the basic assumption that people move in order to access resources. In the case of humans, the most obvious "resource" is employment (Simini et al, 2012(Simini et al, , 2013. Population size has been used as a measure of the "quality" of resources since areas with larger populations (e.g., cities) tend to offer greater opportunities for employment.…”

“…Human tracking studies have been enabled by the growth of the internet coupled with technological innovations such as smartphones and wearables (e.g., smart watches and fitness trackers) that have generated immense and readily accessible geo-referenced data on human mobility and data on human activities such as heart rate monitoring, sports, and sleep tracking (de Arriba-Pérez et al, 2016). These large datasets, amounting in volume to "big data, " are now being analyzed to describe patterns of human movement (Simini et al, 2012(Simini et al, , 2013, features (e.g., sleep, stress, and activities) (de Arriba-Pérez et al, 2016) and interactions (Simini et al, 2012(Simini et al, , 2013Meekan et al, 2017) with a degree of detail, immediacy and precision that was never before possible for any animal species . Moreover, such studies have characterized the movement patterns of humans at global scales for the first time (e.g., Brockmann et al, 2006;Gonzalez et al, 2008).…”

An improved radiation model and its applicability for understanding commuting patterns in Hungary

“…If we assume now that the jobseekers are selective in their choices and they are willing to accept better offers only with a probability 1-λ, we get (Simini et al 2013):…”

“…For λ =0, we recover the original radiation model. Simini et al in 2013Regional Statistics, Vol 6, No 2. 2016DOI: 10.15196/RS06202 showed that this model describes the distribution of the commuting fluxes for the USA better than the simple radiation model.…”

“…The initial model, which in principle is a oneparameter model, was formulated for a discrete settlement structure. It was later generalized (Simini et al 2013) for a continuous population distribution and made more complex by allowing a realistic job selection for the individuals. The new version of the model, the radiation model with selection, is a two-parameter model that offered an improved fit for the commuting data in the USA.…”