Human Mobility Model Research Articles

An attractiveness-based model for human mobility in all spatial ranges

In the past decade, various aspects of human mobility, from individual to population levels in both spatial and time scales, have been studied. However, existing human mobility models still fail to describe activities in all spatial ranges, while in particular, local activities like self-loop fluxes are generally ignored. Moreover, the regional attractiveness, a basic but useful concept in mobility model design, remains difficult to be quantified due to its intricacies. To resolve these two fundamental issues, we introduce a trip competition mechanism to empower a mobility model to estimate population fluxes in all spatial ranges. The model includes attraction scores of regions in concern, obtained via optimizing the working population distributions. Its capability of predicting a variety of mobility patterns is verified by empirical data from three different countries, and its accuracy outperforms those of existing models. The quantified attractiveness is also found to be highly correlated with common socioeconomic indicators, and is able to act as a distinct metric to characterize a region.

Following the footsteps of giants: modeling the mobility of historically notable individuals using Wikipedia

The steady growth of digitized historical information is continuously stimulating new different approaches to the fields of Digital Humanities and Computational Social Science. In this work we use Natural Language Processing techniques to retrieve large amounts of historical information from Wikipedia. In particular, the pages of a set of historically notable individuals are processed to catch the locations and the date of people’s movements. This information is then structured in a geographical network of mobility patterns.We analyze the mobility of historically notable individuals from different perspectives to better understand the role of migrations and international collaborations in the context of innovation and cultural development. In this work, we first present some general characteristics of the dataset from a social and geographical perspective. Then, we build a spatial network of cities, and we model and quantify the tendency to explore of a set of people that can be considered as historically and culturally notable. In this framework, we show that by using a multilevel radiation model for human mobility, we are able to catch important features of migration’s behavior. Results show that the choice of the target migration place for historically and culturally relevant people is limited to a small number of locations and that it depends on the discipline a notable is interested in and on the number of opportunities she/he can find there.

Diagnosing the performance of human mobility models at small spatial scales using volunteered geographical information.

Accurate modelling of local population movement patterns is a core, contemporary concern for urban policymakers, affecting both the short-term deployment of public transport resources and the longer-term planning of transport infrastructure. Yet, while macro-level population movement models (such as the gravity and radiation models) are well developed, micro-level alternatives are in much shorter supply, with most macro-models known to perform poorly at smaller geographical scales. In this paper, we take a first step to remedy this deficit, by leveraging two novel datasets to analyse where and why macro-level models of human mobility break down. We show how freely available data from OpenStreetMap concerning land use composition of different areas around the county of Oxfordshire in the UK can be used to diagnose mobility models and understand the types of trips they over- and underestimate when compared with empirical volumes derived from aggregated, anonymous smartphone location data. We argue for new modelling strategies that move beyond rough heuristics such as distance and population towards a detailed, granular understanding of the opportunities presented in different regions.

An extended exploration and preferential return model for human mobility simulation at individual and collective levels

Human mobility models have prevalent applications in traffic management, urban planning, and disease prevention. Modeling human mobility combining both the statistical physical mechanics and geographical constrains is not fully investigated. In this research, we extend the exploration and preferential return (EPR) model by considering both spatial heterogeneity and distance decay. The extended model takes rank distance in the distance decay function, and is validated in four cities based on the population distribution and the trajectory data. Our model not only reproduces the statistics of human mobility at both the individual and collective levels with high accuracy, but also has a robust prediction at both high and low resolutions. The study demonstrates the potential of applications of the human mobility models aggregating the collective and individual levels factors, and sheds light on the trade-off between ‘simple’ mobility rules and ‘complex’ geographical environments.

Defining a ‘reasonable geographic framework’: Path Distance as native copper provenance in the Arctic, Subarctic, and Northwest Coast

The relative purity of geologic native copper, compared to other raw materials such as obsidian, creates challenges for conducting provenance research. Accurate provenance also depends upon a robust dataset of trace element variation within geological sources. These challenges combine in Northwest North America where three native copper source regions (Central Arctic, Western Subarctic, Northwest Coast) supplied multiple technological traditions in native copper through time. This paper utilizes Path Distance models of human mobility and interaction with the landscape as a proxy for geochemical provenance to estimate a ‘reasonable geographic framework’ (sensu Rapp et al., 2000:96) of the geologic origins of copper artifacts spread across Northwest North America. Generated Path Distance values often link archaeological sites with geologic sources different than when using simple Euclidean distances. Because of their generation using real-world variables to estimate mobility, these values can also be considered a proxy for ‘acquisition cost’ – the cost in time, energy, and social capital spent to acquire raw material – that can frame the analysis of variation in copper and other materials-based technologies in the future.

Habitat suitability and the genetic structure of human populations during the Last Glacial Maximum (LGM) in Western Europe.

Human populations in Western Europe during the Last Glacial Maximum were geographically constrained to glacial refugia by the severity of the climate and ecological risk factors. In this research we use an agent-based model of human mobility and interaction, based on ethnographic and archaeological data, to explore the impact of ecological risk on human population structure via a reconstructed landscape of habitat suitability. The agent-based model allows us to evaluate the size and location of glacial refugia, the size of the populations occupying them and the degree of genetic relatedness between people occupying these areas. To do this, we model the probability of an agent foraging groups’ survival as a function of habitat suitability. The model’s simulated “genomes” (composed of regionally specific genetic markers) allow us to track long-term trends of inter-regional interaction and mobility. The results agree with previous archaeological studies situating a large glacial refugium spanning southern France and northeastern Spain, but we expand on those studies by demonstrating that higher rates of population growth in this central refugium led to continuous out-migration and therefore genetic homogeneity across Western Europe, with the possible exception of the Italian peninsula. These results concur with material culture data from known archaeological sites dating to the Last Glacial Maximum and make predictions for future ancient DNA studies.

Urban Human Mobility

Human mobility is a multidisciplinary field of physics and computer science and has drawn a lot of attentions in recent years. Some representative models and prediction approaches have been proposed for modeling and predicting human mobility. However, multi-source heterogeneous data from handheld terminals, GPS, and social media, provides a new driving force for exploring urban human mobility patterns from a quantitative and microscopic perspective. The studies of human mobility modeling and prediction play a vital role in a series of applications such as urban planning, epidemic control, location-based services, and intelligent transportation management. In this survey, we review human mobility models based on a human-centric angle in a datadriven context. Specifically, we characterize human mobility patterns from individual, collective, and hybrid levels. Meanwhile, we survey human mobility prediction methods from four aspects and then describe recent development respectively. Finally, we discuss some open issues that provide a helpful reference for researchers' future direction. This review not only lays a solid foundation for beginners who want to acquire a quick understanding of human mobility but also provides helpful information for researchers on how to develop a unified human mobility model.

Utilizing general human movement models to predict the spread of emerging infectious diseases in resource poor settings

Human mobility is an important driver of geographic spread of infectious pathogens. Detailed information about human movements during outbreaks are, however, difficult to obtain and may not be available during future epidemics. The Ebola virus disease (EVD) outbreak in West Africa between 2014–16 demonstrated how quickly pathogens can spread to large urban centers following one cross-species transmission event. Here we describe a flexible transmission model to test the utility of generalised human movement models in estimating EVD cases and spatial spread over the course of the outbreak. A transmission model that includes a general model of human mobility significantly improves prediction of EVD’s incidence compared to models without this component. Human movement plays an important role not only to ignite the epidemic in locations previously disease free, but over the course of the entire epidemic. We also demonstrate important differences between countries in population mixing and the improved prediction attributable to movement metrics. Given their relative rareness, locally derived mobility data are unlikely to exist in advance of future epidemics or pandemics. Our findings show that transmission patterns derived from general human movement models can improve forecasts of spatio-temporal transmission patterns in places where local mobility data is unavailable.

A Survey of Human Mobility Models

Human mobility models are key components of various research fields including transportation, mobile networks, disaster management, urban planning, and epidemic modeling. Understanding human mobility has a major role in the realistic evaluation of new approaches to challenges in these fields. For the perspective of networked systems, simulations of the networks with human participants such as opportunistic social networks are highly dependent on human mobility. In this article, we summarize the state of the art for scientific research on human mobility and survey the currently used human mobility models. We discuss the commonly used metrics and data collection techniques. Furthermore, we include a taxonomy of the mobility models according to their main characteristics and classify them. We lastly discuss the general trends, applicability, further research directions and open problems of human mobility modeling.

Deployment and Resource Distribution of Mobile Edge Hosts Based on Correlated User Mobility

Mobile edge computing (MEC) is the next paradigm to support the enormous growth of diverse mobile applications that require high computational power, low latency, and high bandwidth. The user experience can be enhanced beyond the constrained resources limited by the user equipment by offloading computation-intensive tasks to the mobile edge (ME) hosts. However, ensuring service continuity of the offloaded tasks while users are moving from cell to cell is a critical factor to be kept in mind before selecting the locations suitable for ME host deployment. Therefore, in this paper, we present an optimization model to address the resource distribution problem of mobile users in an ME system so that the total number of tasks accomplished is maximized by considering the user mobility patterns in the network. Despite the fact that the correlation of human mobility during peak hours is present in the traces of urban cities, there is a lack of a correlated human mobility model in the existing literature. We first developed a synthetic correlated mobility model to reproduce the human traces of mobility during morning peak hours. We then developed a utilitarian resource distribution algorithm to solve the ME host deployment and the resource distribution problem. We anticipate that the findings of this paper will contribute to the development of a real-time network management and monitoring system in the ME system to detect changes in MEC resource requirements and optimize MEC resource orchestration based on the predicted user mobility and services’ usage demands. Our proposed methods will: 1) become a critical part of the future network management system of 5G that employs MEC; 2) contribute to the standardization of the MEC and 5G platforms; and 3) help network operators reduce the massive cost of deploying and operating a large number of distributed servers in the next-generation mobile networks.

Seasonal and interannual risks of dengue introduction from South-East Asia into China, 2005-2015.

Due to worldwide increased human mobility, air-transportation data and mathematical models have been widely used to measure risks of global dispersal of pathogens. However, the seasonal and interannual risks of pathogens importation and onward transmission from endemic countries have rarely been quantified and validated. We constructed a modelling framework, integrating air travel, epidemiological, demographical, entomological and meteorological data, to measure the seasonal probability of dengue introduction from endemic countries. This framework has been applied retrospectively to elucidate spatiotemporal patterns and increasing seasonal risk of dengue importation from South-East Asia into China via air travel in multiple populations, Chinese travelers and local residents, over a decade of 2005–15. We found that the volume of airline travelers from South-East Asia into China has quadrupled from 2005 to 2015 with Chinese travelers increased rapidly. Following the growth of air traffic, the probability of dengue importation from South-East Asia into China has increased dramatically from 2005 to 2015. This study also revealed seasonal asymmetries of transmission routes: Sri Lanka and Maldives have emerged as origins; neglected cities at central and coastal China have been increasingly vulnerable to dengue importation and onward transmission. Compared to the monthly occurrence of dengue reported in China, our model performed robustly for importation and onward transmission risk estimates. The approach and evidence could facilitate to understand and mitigate the changing seasonal threat of arbovirus from endemic regions.

Modeling real-time human mobility based on mobile phone and transportation data fusion

Even though a variety of human mobility models have been recently developed, models that can capture real-time human mobility of urban populations in a sustainable and economical manner are still lacking. Here, we propose a novel human mobility model that combines the advantages of mobile phone signaling data (i.e., comprehensive penetration in a population) and urban transportation data (i.e., continuous collection and high accuracy). Using the proposed human mobility model, travel demands during each 1-h time window were estimated for the city of Shenzhen, China. Significantly, the estimated travel demands not only preserved the distribution of travel demands, but also captured real-time bursts of mobility fluxes during large crowding events. Finally, based on the proposed human mobility model, a predictive model is deployed to predict crowd gatherings that usually cause severe traffic jams.

A proportional odds model of human mobility and migration patterns

ABSTRACTThe modelling of human mobility and migration patterns has received much attention due to its substantial importance. Despite long-term efforts, we still lack a modelling framework that captures mobility patterns and further obtains a prospective view of movement trends with regards to diverse impacting factors. Here, we propose a proportional odds model of human mobility and migration (POM-HM) that takes a probabilistic approach to model human movements. Our model is based on the migration probability with a log-logistic distribution under the proportional odds assumption. Explanatory variables are introduced into the model by re-parameterizing the probability distribution function. The two resultant functions, namely, the migration strength and cumulative hazard, are used to estimate regional differences among travel fluxes and their tendencies. The performance of the POM-HM in terms of its validity and accuracy is examined and compared with the gravity model and the radiation model. The probability-based modelling framework enables us to investigate regional variations in migrant fluxes consequently further predict potential future patterns. In short, our modelling approach captures the probabilistic nature of human mobility and migration and furthers our understanding of both the spatiotemporal patterns of population movements and the impacts of various driving forces.

A Semantic Model for Human Mobility in an Urban Region

The continuous development and complexity of many modern cities offer many research challenges for urban scientists searching for a better understanding of mobility patterns that happen in space and time. Very large trajectory datasets are often publicly generated thanks to the availability of multiple positioning sensors and location-based services. However, a successful integration of mobility data still requires the development of conceptual and database frameworks that will support appropriate data representation and manipulation capabilities. The research presented in this paper introduces a conceptual modeling and database management approach for representing and analyzing human trajectories in urban spaces. The model considers the spatial, temporal and semantic dimensions in order to take into account the full range of properties that emerge from mobility patterns. Several object data types and data manipulation constructs are developed and experimented on top of an urban dataset testbed currently available in the city of Beijing. The interest of the approach is twofold: first, it clearly appears that very large mobility datasets can be integrated in current extensible GIS; secondly, significant patterns can be derived at the database manipulation level using specifically developed query functions.

BGMM: a body gauss-markov based mobility model for body area networks

Existing mobility models have limitations in their ability to simulate the movement of Wireless Body Area Network (WBAN) since body nodes do not exactly follow either classic mobility models or human contact distributions. In this paper, we propose a new mobility model called Body Gauss-Markov Mobility (BGMM) model, which is oriented specially to WBAN. First, we present the random Gauss-Markov mobility model as the most suitable theoretical basis for developing our new model, as its movement pattern can reveal real human body movements. Next, we examine the transfer of human movement states and derive a simplified mathematical Human Mobility Model (HMM). We then construct the BGMM model by combining the RGMM and HMM models. Finally, we simulate the traces of the new mobility model. We use four direct metrics in our proposed mobility model to evaluate its performance. The simulation results show that the proposed BGMM model performs with respect to the direct mobility metrics and can effectively represent a general WBAN-nodes movement pattern.

A universal model for predicting human migration under climate change: examining future sea level rise in Bangladesh

Climate change is expected to impact the habitability of many places around the world in significant and unprecedented ways in the coming decades. While previous studies have provided estimates of populations potentially exposed to various climate impacts, little work has been done to assess the number of people that may actually be displaced or where they will choose to go. Here we modify a diffusion-based model of human mobility in combination with population, geographic, and climatic data to estimate the sources, destinations, and flux of potential migrants as driven by sea level rise (SLR) in Bangladesh in the years 2050 and 2100. Using only maps of population and elevation, we predict that 0.9 million people (by year 2050) to 2.1 million people (by year 2100) could be displaced by direct inundation and that almost all of this movement will occur locally within the southern half of the country. We also find that destination locations should anticipate substantial additional demands on jobs (594 000), housing (197 000), and food (783 × 109 calories) by mid-century as a result of those displaced by SLR. By linking the sources of migrants displaced by SLR with their likely destinations, we demonstrate an effective approach for predicting climate-driven migrant flows, especially in data-limited settings.

Mobile Matrix: Routing under mobility in IoT, IoMT, and Social IoT

The explosive growth of “things” connected to the Internet (Internet of Things, IoT) raises the question of whether existing ready-to-go networking protocols are enough to cover social and mobile IoT’s demands. IoT aims to interconnect static devices attached to some physical infrastructure. However, mobility is a major factor present in everyday life, and naturally the “things” can move around (Internet of Mobile Things, IoMT) and create social ties (Social IoT, SIoT) in the cyber-physical space. In that context, we present Mobile Matrix (μMatrix), a routing protocol that uses hierarchical IPv6 address allocation to perform any-to-any routing and mobility management without changing a node’s address. In this way, device mobility is transparent to the application level favoring IoMT and SIoT implementation and broader adoption. The protocol has low memory footprint, adjustable control message overhead, and it achieves optimal routing path distortion. Moreover, it does not rely on any particular hardware for mobility detection (a key open issue), such as an accelerometer. Instead, it uses a passive mechanism to detect that a device has moved. We present analytic proofs for the computational complexity and efficiency of μMatrix, as well as an evaluation of the protocol through simulations. We evaluate the protocol performance under human and non-human mobility models. For human mobility, we generated mobility traces using Group Regularity Mobility (GRM) Model, setting its parameters based on real human mobility traces. For the non-human mobility, we propose a new mobility model, to which we refer as Cyclical Random Waypoint (CRWP), where nodes move using a simple Random Waypoint and, eventually, return to their initial position. We compared μMatrix with three baseline protocols: Routing Protocol for low-power and lossy networks (RPL), Mobility Management RPL (MMRPL), and Ad hoc On-Demand Distance Vector (AODV). The results show that μMatrix and RPL offer ≈ 99.9% of bottom-up delivery rate, but only μMatrix offer ≥ 95% of top-down traffic in highly dynamic and mobile scenarios, while other protocols ≤ 75%. Moreover, μMatrix uses up to 65% of the routing table while RPL and AODV fulfill theirs in all scenarios, which leads to poor top-down and any-to-any reliability.

Mathematical models of human mobility of relevance to malaria transmission in Africa

As Africa-wide malaria prevalence declines, an understanding of human movement patterns is essential to inform how best to target interventions. We fitted movement models to trip data from surveys conducted at 3–5 sites throughout each of Mali, Burkina Faso, Zambia and Tanzania. Two models were compared in terms of their ability to predict the observed movement patterns – a gravity model, in which movement rates between pairs of locations increase with population size and decrease with distance, and a radiation model, in which travelers are cumulatively “absorbed” as they move outwards from their origin of travel. The gravity model provided a better fit to the data overall and for travel to large populations, while the radiation model provided a better fit for nearby populations. One strength of the data set was that trips could be categorized according to traveler group – namely, women traveling with children in all survey countries and youth workers in Mali. For gravity models fitted to data specific to these groups, youth workers were found to have a higher travel frequency to large population centers, and women traveling with children a lower frequency. These models may help predict the spatial transmission of malaria parasites and inform strategies to control their spread.

A Spherical Hidden Markov Model for Semantics-Rich Human Mobility Modeling

We study the problem of modeling human mobility from semantic trace data, wherein each GPS record in a trace is associated with a text message that describes the user's activity. Existing methods fall short in unveiling human movement regularities for such data, because they either do not model the text data at all or suffer from text sparsity severely. We propose SHMM, a multi-modal spherical hidden Markov model for semantics-rich human mobility modeling. Under the hidden Markov assumption, SHMM models the generation process of a given trace by jointly considering the observed location, time, and text at each step of the trace. The distinguishing characteristic of SHMM is the text modeling part. We use fixed-size vector representations to encode the semantics of the text messages, and model the generation of the l2-normalized text embeddings on a unit sphere with the von Mises-Fisher (vMF) distribution. Compared with other alternatives like multi-variate Gaussian, our choice of the vMF distribution not only incurs much fewer parameters, but also better leverages the discriminative power of text embeddings in a directional metric space. The parameter inference for the vMF distribution is non-trivial since it involves functional inversion of ratios of Bessel functions. We theoretically prove, for the first time, that: 1) the classical Expectation-Maximization algorithm is able to work with vMF distributions; and 2) while closed-form solutions are hard to be obtained for the M-step, Newton's method is guaranteed to converge to the optimal solution with quadratic convergence rate. We have performed extensive experiments on both synthetic and real-life data. The results on synthetic data verify our theoretical analysis; while the results on real-life data demonstrate that SHMM learns meaningful semantics-rich mobility models, outperforms state-of-the-art mobility models for next location prediction, and incurs lower training cost.

Dynamics of dengue disease with human and vector mobility

Dengue is a vector borne disease transmitted to humans by Aedes aegypti mosquitoes carrying virus of different serotypes. Dengue exhibits complex spatial and temporal dynamics, influenced by various biological, human and environmental factors. In this work, we study the dengue spread for a single serotype (DENV-1) including statistical models of human mobility with exponential step length distribution, by using reaction–diffusion equations and Stochastic Cellular Automata (SCA) approach. We analyze the spatial and temporal spreading of the disease using parameters from field studies. We choose mosquito density data from Ahmedabad city as a proxy for climate data in our SCA model. We find an interesting result that although human mobility makes the infection spread faster, there is an apparent early suppression of the epidemic compared to immobile humans. The disease extinction time is lesser when human mobility is included.