Understanding Of Human Mobility Research Articles

Causal discovery reveals complex patterns of drought-induced displacement

The increasing frequency and severity of droughts present a significant risk to vulnerable regions of the globe, potentially leading to substantial human displacement in extreme situations. Drought-induced displacement is a complex and multifaceted issue that can perpetuate cycles of poverty, exacerbate food and water scarcity, and reinforce socio-economic inequalities. However, our understanding of human mobility in drought scenarios is currently limited, inhibiting accurate predictions and effective policy responses. Drought-induced displacement is driven by numerous factors and identifying its key drivers, causal-effect lags, and consequential effects is often challenging, typically relying on mechanistic models and qualitative assumptions. This paper presents a novel, data-driven methodology, grounded in causal discovery, to retrieve the drivers of drought-induced displacement within Somalia from 2016 to 2023. Our model exposes the intertwined vulnerabilities and the leading times that connect drought impacts, water and food security systems along with episodes of violent conflict, emphasizing that causal mechanisms change across districts. These findings pave the way for the development of algorithms with the ability to learn from human mobility data, enhancing anticipatory action, policy formulation, and humanitarian aid.

Simulating human mobility with a trajectory generation framework based on diffusion model

Most mobility modeling methods are designed to solve specific tasks, leading to questions regarding their deficiency in generalizability. Inspired by the bloom of foundation models, we proposed a Trajectory Generation framework based on the Diffusion Model (TrajGDM) to capture the universal mobility pattern in a trajectory dataset by learning the trajectory generation process. The process is modeled as a step-by-step uncertainty-reducing process, in which a deep learning network with a novel training method is proposed to learn from the process. We compared the proposed trajectory generation method with six baselines on two public trajectory datasets. The results showed that the similarity between the generated and real trajectory movements measured by the Jensen-Shannon Divergence improved significantly on both datasets. Moreover, we applied zero-shot inferences on two basic trajectory tasks: trajectory prediction and trajectory reconstruction. The accuracy improved by a maximum of 25.6% on two tasks. The universal mobility pattern that is suitable for solving multiple trajectory tasks is verified, inferring the strong generalizability of our model. Finally, the study provides insights into artificial intelligence’s understanding of human mobility by exploring the way the model maps the trajectory in the latent space into reality.

Hidden urban patterns: Existential discovery of urban patterns based on traffic and virtual space

Human mobility is the basis for shaping individual and social relationships. With the advancement of the digital age and smart cities, the use of mobile phone data and the location of users is considered an important tool for collecting urban data, which, due to its purity, can form the basis for studies on human mobility. The existing problem, the lack of proper identification and understanding of human mobility, is the most important factor in the design of the city. This problem is introduced as the starting point of the problems in the city because the beginning of the problems of urban life is related to the beginning of human mobility, and this is necessary for urban life. Therefore, the identification of the pattern of human mobility as urban exploration and the discovery of intra-urban relations play an important role. This study focuses on how traffic indicators and daily trips based on mobile phone data influence the formation of congested places based on human mobility. This mobility is determined based on the generated and recorded trips. The proposed pattern is based on real-time data.

Exploring the spatial characteristics of the human mobility network in rural settings of China's Greater Bay Area

Human mobility is an important carrier of material flow, information flow, capital flow, and technology flow between locations. A better understanding of human mobility in rural areas could inform efforts to achieve rural revitalization. However, there have been limited evaluations of the characteristics of human mobility in rural areas. Based on human movements estimated from mobile phone data, we constructed an inter-town human mobility network and applied a complex network analysis method to study the spatial characteristics of the human mobility network in rural areas, with China's Greater Bay Area as a case study. We found that rural towns with high centrality were mostly located at the convergence of developed megacities. Rural towns with most connections were not necessarily the most central in the inter-town human mobility networks, leading to anomalous values of the centrality. We demonstrated that the anomaly of centrality values emerged due to the community structure of the network. We identified nine communities in the network and found that the geographical borders of communities partly matched the urban administrative borders. However, the results also showed that many rural towns on the fringes of a city were dissolved in their adjacent developed cities, independent of the urban administrative boundary effect. The formation of communities cannot be explained only by the impacts of urban administrative boundaries, but also by the growth of large cities and the geographic dispersal of economic activities. Finally, we identified seven roles of rural towns based on their pattern of intra-community and inter-community connections. These findings provide important insights into the pattern of interaction among rural towns.

User-station attention inference using smart card data: a knowledge graph assisted matrix decomposition model

Understanding human mobility in urban areas is important for transportation, from planning to operations and online control. This paper proposes the concept of user-station attention, which describes the user’s (or user group’s) interest in or dependency on specific stations. The concept contributes to a better understanding of human mobility (e.g., travel purposes) and facilitates downstream applications, such as individual mobility prediction and location recommendation. However, intrinsic unsupervised learning characteristics and untrustworthy observation data make it challenging to estimate the real user-station attention. We introduce the user-station attention inference problem using station visit counts data in public transport and develop a matrix decomposition method capturing simultaneously user similarity and station-station relationships using knowledge graphs. Specifically, it captures the user similarity information from the user-station visit counts matrix. It extracts the stations’ latent representation and hidden relations (activities) between stations to construct the mobility knowledge graph (MKG) from smart card data. We develop a neural network (NN)-based nonlinear decomposition approach to extract the MKG relations capturing the latent spatiotemporal travel dependencies. The case study uses both synthetic and real-world data to validate the proposed approach by comparing it with benchmark models. The results illustrate the significant value of the knowledge graph in contributing to the user-station attention inference. The model with MKG improves the estimation accuracy by 35% in MAE and 16% in RMSE. Also, the model is not sensitive to sparse data provided only positive observations are used.

Editorial for the Special Issue on Migration, Education, and Youth

In this special issue, we present a selection of papers discussing from a multidisciplinary perspective and referring to various geographies, the particularities of young migrants, their decisions to return, determinants of their migration, social networks, integration, education, and their role in the diaspora. Our goal is to continue the debate on youth migration and point out the questions and theoretical challenges to further our understanding of human mobility.

Examining the Discrepancies between Self-Reported and Actual Commuting Behavior at the Individual Level

For decades, transportation researchers have used survey data to understand the factors that affect travel-related choices. Nowadays, travel surveys lay the foundation of travel behavior analysis for transportation modeling, planning, and policy-making. The development of information technology for urban sensing has enabled substantial improvements to be made in survey-elicited and passive mobility data collection. Actively collected and passive data are very different, and being able to compare and integrate them could allow stakeholders to achieve a greater understanding of human mobility. The comparison between survey self-reported travel behavior and actual travel behavior revealed by urban and mobile systems provides us with the opportunity to find potential discrepancies. Previous work has examined these discrepancies mostly at the population level. An individual-level investigation of these discrepancies could provide many benefits, from increasing our understanding of survey and passive data accuracy and collection, to designing personalized transportation services. In this study, the discrepancies between self-reported and observed travel behavior are analyzed at both the individual and aggregated level by utilizing the available mobility data, namely, survey-based commuting diaries and passive mobility records. We propose a group of discrepancy metrics for commuting activities for which we have available and comparable data, and apply the framework to an empirical analysis at the Massachusetts Institute of Technology in Cambridge, U.S.A. Our results show that survey-elicited commuting diaries are quite reliable when examining overall commuting trends, whereas passive mobility data are more suitable for investigating individual-level commuting behavior. Furthermore, we identify the association between discrepancies in commuting behavior and certain individual characteristics, for example, employee type and age.

The pilot application of geochemical sourcing to an inland Pilbara archaeological landscape in north-western Australia

Past human mobility, often reconstructed using stone artefact assemblages, is an important component of Australian archaeology. Tracing the transport of stone artefacts enables direct measures of the direction and distance of past Aboriginal movement across landscapes. Sourcing transported stone artefacts to their geological point of origin therefore has the potential to significantly improve our understanding of human mobility. Beyond this, the identification of artefact source locations is also critical for stone artefact analyses since raw material factors strongly condition assemblage formation. However, relatively little stone sourcing has been completed in Australia. This is particularly the case for the inland Pilbara, north-western Australia, where almost no sourcing work has been published. This paper presents the results of a limited pilot study which aimed to test the application of petrographic and in situ laser ablation inductively coupled plasma mass spectrometry (LA-ICPMS) analytical techniques to the sourcing of transported artefacts from three Pilbara quarried outcrops to two non-quarried open surface sites. The results indicate that geochemical interelement ratios (e.g., La/Nd and U/Th) can discriminate between both quarry sites and artefacts. A clear petrographic and geochemical match between a quarry and one stone artefact discarded at a non-quarried site was found, and different samples collected from the same quarry yielded overlapping geochemical signatures. This suggests that LA-ICPMS has the potential to elucidate artefact transport (as a proxy for the movement of people) between locations, effectively linking these places as part of a behavioural system. While a larger database of artefact and quarry specimens is needed to fully explore both the efficacy of the approach and the archaeological implications, this study suggests that sourcing techniques hold significant promise for reconstructing past human mobility in the Pilbara and for demonstrating landscape-scale connections between sites.

A theory of migration: the aspirations-capabilities framework

This paper elaborates an aspirations–capabilities framework to advance our understanding of human mobility as an intrinsic part of broader processes of social change. In order to achieve a more meaningful understanding of agency and structure in migration processes, this framework conceptualises migration as a function of aspirations and capabilities to migrate within given sets of perceived geographical opportunity structures. It distinguishes between the instrumental (means-to-an-end) and intrinsic (directly wellbeing-affecting) dimensions of human mobility. This yields a vision in which moving and staying are seen as complementary manifestations of migratory agency and in which human mobility is defined as people’s capability to choose where to live, including the option to stay, rather than as the act of moving or migrating itself. Drawing on Berlin’s concepts of positive and negative liberty (as manifestations of the widely varying structural conditions under which migration occurs) this paper conceptualises how macro-structural change shapes people’s migratory aspirations and capabilities. The resulting framework helps to understand the complex and often counter-intuitive ways in which processes of social transformation and ‘development’ shape patterns of migration and enable us to integrate the analysis of almost all forms of migratory mobility within one meta-conceptual framework.

The mobility laws of location-based games

Mobility is a fundamental characteristic of human society that shapes various aspects of our everyday interactions. This pervasiveness of mobility makes it paramount to understand factors that govern human movement and how it varies across individuals. Currently, factors governing variations in personal mobility are understudied with existing research focusing on explaining the aggregate behaviour of individuals. Indeed, empirical studies have shown that the aggregate behaviour of individuals follows a truncated Lévy-flight model, but little understanding exists of the laws that govern intra-individual variations in mobility resulting from transportation choices, social interactions, and exogenous factors such as location-based mobile applications. Understanding these variations is essential for improving our collective understanding of human mobility, and the factors governing it. In this article, we study the mobility laws of location-based gaming—an emerging and increasingly popular exogenous factor influencing personal mobility. We analyse the mobility changes considering the popular PokémonGO application as a representative example of location-based games and study two datasets with different reporting granularity, one captured through location-based social media, and the other through smartphone application logging. Our analysis shows that location-based games, such as PokémonGO, increase mobility—in line with previous findings—but the characteristics governing mobility remain consistent with a truncated Lévy-flight model and that the increase can be explained by a larger number of short-hops, i.e., individuals explore their local neighborhoods more thoroughly instead of actively visiting new areas. Our results thus suggest that intra-individual variations resulting from location-based gaming can be captured by re-parameterization of existing mobility models.

Analysis of urban mobility in South Florida using Uber Movement

Ridesharing services linked to mobile devices are increasingly playing a major role in human mobility in urban areas. With the recent release of Uber Movement data for certain large cities it is now possible to analyze urban mobility at finer spatial scales. In the present study, we used aggregate Uber trip data from 2016 to 2019 for the Miami metropolitan area. The peak hour of Uber trips and mean travel times were calculated using harmonic analysis. The total number of trips were expectedly higher on the weekdays compared to weekends for both sources and destinations. In addition, there was a distinct spatial clustering along major thoroughfares connecting the entire study area from north to south, and the two main airports of the study area, Miami International Airport and Fort Lauderdale Hollywood International Airport. The main thoroughfares along which the Uber trips were clustered also overlap with the two modes of mass transit, metro rail and Tri Rail. This is indicative of most riders using mass transit to the closest station of their destination, and then using Uber as a connection to the location of interest. The results of our study will be useful for urban planning and better understanding of human mobility across the metropolitan area.

Comparative Analysis of Travel Patterns from Cellular Network Data and an Urban Travel Demand Model

Data on travel patterns and travel demand are an important input to today’s traffic models used for traffic planning. Traditionally, travel demand is modelled using census data, travel surveys, and traffic counts. Problems arise from the fact that the sample sizes are rather limited and that they are expensive to collect and update the data. Cellular network data are a promising large-scale data source to obtain a better understanding of human mobility. To infer travel demand, we propose a method that starts by extracting trips from cellular network data. To find out which types of trips can be extracted, we use a small-scale cellular network dataset collected from 20 mobile phones together with GPS tracks collected on the same device. Using a large-scale dataset of cellular network data from a Swedish operator for the municipality of Norrköping, we compare the travel demand inferred from cellular network data to the municipality’s existing urban travel demand model as well as public transit tap-ins. The results for the small-scale dataset show that, with the proposed trip extraction methods, the recall (trip detection rate) is about 50% for short trips of 1-2 km, while it is 75–80% for trips of more than 5 km. Similarly, the recall also differs by a travel mode with more than 80% for public transit, 74% for car, but only 53% for bicycle and walking. After aggregating trips into an origin-destination matrix, the correlation is weak (R2<0.2) using the original zoning used in the travel demand model with 189 zones, while it is significant with R2=0.82 when aggregating to 24 zones. We find that the choice of the trip extraction method is crucial for the travel demand estimation as we find systematic differences in the resulting travel demand matrices using two different methods.

Complete trajectory reconstruction from sparse mobile phone data

Mobile phone data are a popular source of positioning information in many recent studies that have largely improved our understanding of human mobility. These data consist of time-stamped and geo-referenced communication events recorded by network operators, on a per-subscriber basis. They allow for unprecedented tracking of populations of millions of individuals over long periods that span months. Nevertheless, due to the uneven processes that govern mobile communications, the sampling of user locations provided by mobile phone data tends to be sparse and irregular in time, leading to substantial gaps in the resulting trajectory information. In this paper, we illustrate the severity of the problem through an empirical study of a large-scale Call Detail Records (CDR) dataset. We then propose Context-enhanced Trajectory Reconstruction, a new technique that hinges on tensor factorization as a core method to complete individual CDR-based trajectories. The proposed solution infers missing locations with a median displacement within two network cells from the actual position of the user, on an hourly basis and even when as little as 1% of her original mobility is known. Our approach lets us revisit seminal works in the light of complete mobility data, unveiling potential biases that incomplete trajectories obtained from legacy CDR induce on key results about human mobility laws, trajectory uniqueness, and movement predictability.

A Two-Level Approach to Characterizing Human Activities from Wearable Sensor Data

The rapid emergence of new technologies in recent decades has opened up a world of opportunities for a better understanding of human mobility and behavior. It is now possible to recognize human movements, physical activity and the environments in which they take place. And this can be done with high precision, thanks to miniature sensors integrated into our everyday devices. In this paper, we explore different methodologies for recognizing and characterizing physical activities performed by people wearing new smart devices. Whether it's smartglasses, smartwatches or smartphones, we show that each of these specialized wearables has a role to play in interpreting and monitoring moments in a user's life. In particular, we propose an approach that splits the concept of physical activity into two sub-categories that we call micro-and macro-activities. Micro-and macro-activities are supposed to have functional relationship with each other and should therefore help to better understand activities on a larger scale. Then, for each of these levels, we show different methods of collecting, interpreting and evaluating data from different sensor sources. Based on a sensing system we have developed using smart devices, we build two data sets before analyzing how to recognize such activities. Finally, we show different interactions and combinations between these scales and demonstrate that they have the potential to lead to new classes of applications, involving authentication or user profiling.

Understanding Metropolitan Crowd Mobility via Mobile Cellular Accessing Data

Understanding crowd mobility in a metropolitan area is extremely valuable for city planners and decision makers. However, crowd mobility is a relatively new area of research and has significant technical challenges: lack of large-scale fine-grained data, difficulties in large-scale trajectory processing, and issues with spatial resolution. In this article, we propose a novel approach for analyzing crowd mobility on a “city block” level. We first propose algorithms to detect homes, working places, and stay regions for individual user trajectories. Next, we propose a method for analyzing commute patterns and spatial correlation at a city block level. Using mobile cellular accessing trace data collected from users in Shanghai, we discover commute patterns, spatial correlation rules, as well as a hidden structure of the city based on crowd mobility analysis. Therefore, our proposed methods contribute to our understanding of human mobility in a large metropolitan area.

How Short Is Long Enough? Modeling Temporal Aspects of Human Mobility Behavior Using Mobile Phone Data

Time–location data collected from location-sensing technologies have the potential to advance our understanding of human mobility. Existing human activity studies tend to ignore a critical issue in data collection—the time period for which the activity data will be collected. Our study investigated this significant gap in the literature on temporal aspects of human mobility behavior—how many days constitute a period long enough to capture individuals’ highly organized activity episodes and how they vary among individuals with heterogeneous demographic and social-economic characteristics. To determine a minimum number of days to capture individuals’ highly organized activity episodes in activity space, we examined a distribution of Kullback–Leibler divergence indexes. To evaluate the differences in the minimal number of observation days per subgroup whose demographic and economic characteristics are heterogenous, we used a Bayesian profile regression model. Our study showed that the estimated minimum number of days required to capture routine activity patterns was 13.5 days with a standard deviation of 6.64. We found that participant’s age, employment status, size of household, and accessibility to downtown, food, and physical activity, as well as economic status of residential environment, are important factors that affect temporal aspects of mobility behavior. Key Words: Bayesian profile regression, human mobility, Kullback–Leibler divergence, mobile phone data, temporal regularity.

Discovering Interest Based Mobile Communities

Human mobility has attracted lots of attention in the domain of wireless communication technologies and behavioral sciences. Exploring and analyzing human movements that are changing over time, relating to user habits and depending on spacial aspect is a challenging problem. In this paper, we are interested in discovering communities of mobile users and studying how do communities provide accurate knowledge to analysis the different forms of human mobility. The basic idea of our work is to model the behaviour of users with strong social characteristics regarding the context of their location histories to explore a similar interest of people by mining their mobiles communities. The proposed analysis illustrates in what way a common interest of a group of individuals can create better understanding of human mobility. Realistic models based on these interest based communities can be the basis for applications as recommendation system or wireless networks management.

Understanding individual routing behaviour.

Knowing how individuals move between places is fundamental to advance our understanding of human mobility (González et al. 2008 Nature 453, 779-782. (doi:10.1038/nature06958)), improve our urban infrastructure (Prato 2009 J. Choice Model. 2, 65-100. (doi:10.1016/S1755-5345(13)70005-8)) and drive the development of transportation systems. Current route-choice models that are used in transportation planning are based on the widely accepted assumption that people follow the minimum cost path (Wardrop 1952 Proc. Inst. Civ. Eng. 1, 325-362. (doi:10.1680/ipeds.1952.11362)), despite little empirical support. Fine-grained location traces collected by smart devices give us today an unprecedented opportunity to learn how citizens organize their travel plans into a set of routes, and how similar behaviour patterns emerge among distinct individual choices. Here we study 92 419 anonymized GPS trajectories describing the movement of personal cars over an 18-month period. We group user trips by origin-destination and we find that most drivers use a small number of routes for their routine journeys, and tend to have a preferred route for frequent trips. In contrast to the cost minimization assumption, we also find that a significant fraction of drivers' routes are not optimal. We present a spatial probability distribution that bounds the route selection space within an ellipse, having the origin and the destination as focal points, characterized by high eccentricity independent of the scale. While individual routing choices are not captured by path optimization, their spatial bounds are similar, even for trips performed by distinct individuals and at various scales. These basic discoveries can inform realistic route-choice models that are not based on optimization, having an impact on several applications, such as infrastructure planning, routing recommendation systems and new mobility solutions.

Simulating human mobility patterns in urban areas

With the rise of smart cities people are moving within urban spaces and still be able to pervasively interact with information, services, city’s resources and other people. In such a highly connected scenario, smartphones and other wireless portable devices are carried by humans, exhibit the same mobility behaviour of their human carriers and their movements strongly impact on the underlying network operation and performance. The understanding of human mobility in an urban space has become crucial to optimize the network management, to plan the adaptive allocation of critical resources and ensure constant quality of the user experience. This paper takes a first step in the direction of the design of a mobility model meeting behavioural and scale requirements of modern smart cities. We envision a smart city as a collection of places, each representing a Point of Interest (PoI) with specific value for single individuals and for a set of them. As a consequence, each individual has his/her own mobility footprint, while few of them share similar mobility patterns. By simulating the mobility of each individual across city’s places, we will be able to properly describe human mobility and social behaviour in urban spaces, and to extract all needed information about how city’s resources and services are accessed. The extensive use of CDR, GPS and WiFi traces, enables us to analyse the characteristics of city's Points of Interest (PoI), classify them for each individual according to their importance and study how the individuals move across them. The common features observed are the key points to build a metropolitan mobility simulator able to reproduce the regularity in spatio-temporal behaviour of mobile users and also how city sociality is built around PoIs of the city. The simulator exhibits high flexibility and can be applied in wide geographical and population scales.

Mining Human Mobility in Location-Based Social Networks

In recent years, there has been a rapid growth of location-based social networking services, such as Foursquare and Facebook Places, which have attracted an increasing number of users and greatly enriched their urban experience. Typical location-based social networking sites allow a user to check at a real-world POI (point of interest, e.g., a hotel, restaurant, theater, etc.), leave tips toward the POI, and share the check-in with their online friends. The check-in action bridges the gap between real world and online social networks, resulting in a new type of social networks, namely location-based social networks (LBSNs). Compared to traditional GPS data, location-based social networks data contains unique properties with abundant heterogeneous information to reveal human mobility, i.e., and where a user (who) has been to for what, corresponding to an unprecedented opportunity to better understand human mobility from spatial, temporal, social, and content aspects. The mi ing and understanding of human mobility can further lead to effective approaches to improve current location-based services from mobile marketing to recommender systems, providing users more convenient life experience than before. This book takes a data mining perspective to offer an overview of studying human mobility in location-based social networks and illuminate a wide range of related computational tasks. It introduces basic concepts, elaborates associated challenges, reviews state-of-the-art algorithms with illustrative examples and real-world LBSN datasets, and discusses effective evaluation methods in mining human mobility. In particular, we illustrate unique characteristics and research opportunities of LBSN data, present representative tasks of mining human mobility on location-based social networks, including capturing user mobility patterns to understand when and where a user commonly goes (location prediction), and exploiting user preferences and location profiles to inv stigate where and when a user wants to explore (location recommendation), along with studying a user's check-in activity in terms of why a user goes to a certain location.