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In this paper, we extend some ideas of statistical physics to describe the properties of human mobility. From a physical point of view, we consider the statistical empirical laws of private cars mobility, taking advantage of a GPS database which contains a sampling of the individual trajectories of 2% of the whole vehicle population in an Italian region. Our aim is to discover possible "universal laws" that can be related to the dynamical cognitive features of individuals. Analyzing the empirical trip length distribution we study if the travel time can be used as universal cost function in a mesoscopic model of mobility. We discuss the implications of the elapsed times distribution between successive trips that shows an underlying Benford's law, and we study the rank distribution of the average visitation frequency to understand how people organize their daily agenda. We also propose simple stochastic models to suggest possible explanations of the empirical observations and we compare our results with analogous results on statistical properties of human mobility presented in the literature.

Partly due to the difficulty of the access to a worldwide dataset that simultaneously captures the location history and social networks, our understanding of the relationship between human mobility and the social ties has been limited. However, this topic is essential for a deeper study from human dynamics and social networks aspects. In this paper, we examine the location history data and social networks data of 712 email users and 399 offline events users from a map-editing based social network website. Based on these data, we expand all our experiment both from individual aspect and community aspect. We find that the physical distance is still the most influential factor to social ties among the nine representative human mobility features extracted from our GPS trajectory dataset, although Internet revolution has made long-distance communication dramatically faster, easier and cheaper than ever before, and in turn, partly expand the physical scope of social networks. Furthermore, we find that to a certain extent, the proximity of South–North direction is more influential than East–West direction to social ties. To the our best of our knowledge, this difference between South–North and East–West is the first time to be raised and quantitatively supported by a large dataset. We believe our findings on the interplay of human mobility and social ties offer a new perspective to this field of study.

In recent years, the dynamic spread of information has captured researchers’ attention. Therefore, identifying influential spreaders of information has become a fundamental element of information spreading research. Many studies have measured the influence of spreaders by considering the centrality indexes of network topology characteristics, such as degree, betweenness and closeness centrality. Additionally, some works have identified influential spreaders by analyzing human mobility characteristics such as contact frequency, contact time and inter-contact time. In this paper, we mainly explore the influence of the step size and radius of gyration on information spreading. Using a real and large-scale dataset of human mobility, we apply the susceptible-infected-recovered (SIR) model to investigate the spread of information. The simulation result shows that the influence of information spreading does not increase with the increase in the step size or radius of gyration of spreaders. Instead, both the step size and radius of gyration have a great influence on the spread of information when they are near the median value. Regardless of whether they have a large or small value, their influence on the spread of information is small. Therefore, the step size and radius of gyration of spreaders can be used to control or guide the spread of information.

In every country except China, COVID-19 first infection cases were imported by travelers, which are either people coming back to their own country or visiting foreigners (international or external tourists). In a global and regional phenomenological analysis of COVID-19 spread, we assume that tourism inflow is a trigger mechanism of worldwide dissemination at the pandemic onset days. Taking into account all countries, a convenient common-time origin timeline was employed as if the beginning of the epidemic would have occurred simultaneously in every country. We obtained very good statistical Pearson and Spearman correlations between accumulated infected cases by country and a positive power of the product $TP$, where $T$ is the tourism inflow before the pandemic and $P$ is the country population.

In the USA, COVID-19 first infection cases were imported by external travelers. At the epidemic onset days, we assume that the disease partially spreads due to domestic passengers air transportation in its densely connected airport network. Taking into account all USA states, we arranged COVID-19 infected cases data in a convenient common time origin timeline as if the beginning of the epidemic would have occurred simultaneously in every state. Looking for a trend between cases and air passengers, we obtained with this timeline very good statistical Pearson and Spearman correlations between accumulated infected cases by state and a positive power of the product $TP$, where $T$ is the domestic flight passengers (travelers) inflow by state before the epidemic and $P$ is its population. We also found a good correlation between percentages of urban area by state and their COVID-19 daily new cases growth rates at onset days.

Understanding the spatial interactions of human mobility is crucial for urban planning, traffic engineering, as well as for the prevention and control of infectious diseases. Although many models have been developed to model human mobility, it is not clear whether such models could also capture the traveling mechanisms across different time periods (e.g. workdays, weekends or holidays). With one-year long nationwide location-based service (LBS) data in China, we investigate the spatiotemporal characteristics of population movements during different time periods, and make thorough comparisons for the applicability of five state-of-the-art human mobility models. We find that population flows show significant periodicity and strong inequality across temporal and spatial distribution. A strong “backflow” effect is found for cross-city movements before and after holidays. Parameter fitting of gravity models reveals that travels in different type of days consider the attractiveness of destinations and cost of distance differently. Surprisingly, the comparison indicates that the parameter-free opportunity priority selection (OPS) model outperforms other models and is the best to characterize human mobility in China across all six different types of days. However, there is still an urgent need for development of more dedicated models for human mobility on weekends and different types of holidays.

In this research, we analyze the air transportation network of the United States of America using databases with passenger activity containing detailed records of domestic flights between airports. Different properties obtained from the origin-destination matrix in the air transport network are studied from 2011 to 2020. The results are compared with a model based on the gravity law for human mobility defined in terms of transition probabilities and travel distances. The predictions of this model are confirmed through Monte Carlo simulations. Our findings reproduce the dynamics of passengers in the airport transportation network.

Urban bus transit is a major mode of transportation in modern cities and plays an important role in mitigating the traffic pressure in urban road networks. We used smartcard data collected by three-million bus passengers in Shenzhen, a major southern city of China, to study the statistical properties and dynamics of bus passenger flows. In this study, the recorded passenger flows were cross-grained into each 1 km × 1 km square grids to avoid large flow variations at a single bus station. The temporal and spatial patterns of passenger flows were analyzed and a machine learning-based model for passenger flow prediction was generated.

In this paper, we propose a new dynamical system model pertaining to Dengue transmission, and investigate its consequent morphology. We present and study various ramifications of our mathematical model for Dengue spread, encapsulated in a spatio-temporal differential system made of reaction–diffusion equations. Diffusion terms are incorporated into the said model by using specific derivations for infected mosquitoes, and infected humans, as well. Moreover, mechanisms for the nearest neighbor(s) infections are integrated into the model. Furthermore, using adaptive multigrid finite difference with decoupling and quasi-linearization techniques, we investigate two main factors for Dengue spatial propagation. We determine the effects of temperature variations, and the mobility of infectious agents, be they mosquitoes or humans. Finally, the proposed model-based analytico-numerical results are obtained, and rendered in graphical profiles, which show the major role the climate temperature and the mobility of infected humans have on the spread and speed of the disease. The consequent proposed model outcomes and health-based ramifications are then raised, discussed, and then validated.

Avian Flu – A Never Ending Disaster?

Avian Flu and the Pandemic Threat.

The Risk of an H5N1 Pandemic in Humans is Possible but Unlikely.

The interpretation and characterization of universal scaling laws in human mobility and activity are subjects of active research. For the better understanding, instead of the statistical approach we have examined the temporal patterns of human daily motion using location data. The trajectories were measured continuously and with even sampling (1 measurement per minute), using GPS and Wi-Fi/mobile Internet signals of the participant’s smartphone, we have analyzed the few-week-long signals of displacements between two subsequent samplings in frequency domain. Our results had shown that 1/$f$ type noise is observable over the frequency of the daily rhythm of motion and its harmonics. We point out several technical questions about the measurement and data processing required for further detailed analysis. Furthermore, our new observation could help in the investigation of the underlying dynamics of human motion and opens several theoretical questions about the relationship between the spatial and temporal universality.

In recent decades, a strong focus has emerged on exploring the scale-independent nature of our daily spatial motion. Similarly, heavy-tailed distributions have been observed for human locomotor activity, measured by actigraphs in medical fields. We recently proved that the raw acceleration data and also the activity signals calculated from them in diverse ways exhibit a general spectral characteristic; 1/f noise is observable above a certain cutoff frequency, while white noise and peaks corresponding to daily rhythms are visible at lower frequencies. We show that this pattern is strikingly similar to what we found earlier for GPS data and this similarity raises fundamental questions like what is the relation between human mobility and physical activity, what are the benefits of the analysis from this perspective, and how it helps us to better understand and model the scale-independent nature of human dynamics.

The rise of socio-technical systems in which humans interact with various forms of Artificial Intelligence, including assistants and recommenders, multiplies the possibility for the emergence of large-scale social behavior, possibly with unintended negative consequences. In this work, we discuss a particularly interesting case, i.e., navigation services’ impact on urban emissions, showing through simulations that the sum of many individually “optimal” choices may have unintended negative outcomes because such choices influence and interfere with each other on top of shared resources. To prove this point, we demonstrate how the introduction of a random component in the path suggestion phase may help to relieve the effect of collective and individual choices on the urban environment in terms of urban emissions.