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Human Mobility: Models and Applications
Abstract
Recent years have witnessed an explosion of extensive geolocated datasets related to human movement, enabling scientists to quantitatively study individual and collective mobility patterns, and to generate models that can capture and reproduce the spatiotemporal structures and regularities in human trajectories. The study of human mobility is especially important for applications such as estimating migratory flows, traffic forecasting, urban planning, and epidemic modeling. In this survey, we review the approaches developed to reproduce various mobility patterns, with the main focus on recent developments. This review can be used both as an introduction to the fundamental modeling principles of human mobility, and as a collection of technical methods applicable to specific mobility-related problems. The review organizes the subject by differentiating between individual and population mobility and also between short-range and long-range mobility. Throughout the text the description of the theory is intertwined with real-world applications.
... It is crucial in many applications such as travel recommendation and optimization (Shi et al., 2019;Khaidem et al., 2020), early warning of potential public emergencies (Barlacchi et al., 2017;Canzian & Musolesi, 2015;Pappalardo et al., 2016;Voukelatou et al., 2020;Pappalardo et al., 2023), estimation of urban emissions Cornacchia et al., 2022;Arora et al., 2021), location-aware advertisements and geo-marketing, and recommendation of friends in social network platforms (Zhu et al., 2015;Burbey & Martin, 2012;Wu et al., 2018;Zheng et al., 2018;Zhao, 2020). Predicting an individual's location is challenging as it requires capturing mobility patterns (Barbosa et al., 2018;Luca et al., 2021) and combining heterogeneous data sources to model the factors influencing displacements (e.g., weather, transportation mode, preferences for specific points of interest). ...
... Several studies measure the limits of predictability of human mobility (Barbosa et al., 2018;Luca et al., 2021). Song et al. (2010) analyze mobility traces of anonymized mobile phone users to find that 93% of the movements are potentially predictable. ...
... For example, some test and training trajectories may belong to the same individual. Since human mobility is routinary, an individual's trajectories are similar to each other (Barbosa et al., 2018;Schläpfer et al., 2021), leading to scenarios (1) and (2) above. Given this discussion, we investigate the extent to which the overlap between trajectories in the test and training sets influences the model's ability to generalize. ...
Next-location prediction, consisting of forecasting a user’s location given their historical trajectories, has important implications in several fields, such as urban planning, geo-marketing, and disease spreading. Several predictors have been proposed in the last few years to address it, including last-generation ones based on deep learning. This paper tests the generalization capability of these predictors on public mobility datasets, stratifying the datasets by whether the trajectories in the test set also appear fully or partially in the training set. We consistently discover a severe problem of trajectory overlapping in all analyzed datasets, highlighting that predictors memorize trajectories while having limited generalization capacities. We thus propose a methodology to rerank the outputs of the next-location predictors based on spatial mobility patterns. With these techniques, we significantly improve the predictors’ generalization capability, with a relative improvement in the accuracy up to 96.15% on the trajectories that cannot be memorized (i.e., low overlap with the training set).
... Critical repeated patterns emerge when studying thousands of migrations. For example, most migration is across short distances, between nearby areas [20,21,22,23]. Also, large destinations attract disproportionally more migrants than smaller ones [24,25]. ...
... The gravity model captures the impact of size at the origin and destination countries and their distance [26,27,20,65,66]. Gravity has been used, for example, to model trade between countries and cultural distances or frictions between distinct locations [26,27,23]. The gravity model, however, does not quantify the intensity of migration but gives only a description of the assortativity. ...
Migration's impact spans various social dimensions, including demography, sustainability, politics, economy and gender disparities. Yet, the decision-making process behind migrants choosing their destination remains elusive. Existing models primarily rely on population size and travel distance to explain flow fluctuations, overlooking significant population heterogeneities. Paradoxically, migrants often travel long distances and to smaller destinations if their diaspora is present in those locations. To address this gap, we propose the diaspora model of migration, incorporating intensity (the number of people moving to a country) and assortativity (the destination within the country). Our model considers only the existing diaspora sizes in the destination country, influencing the probability of migrants selecting a specific residence. Despite its simplicity, our model accurately reproduces the observed stable flow and distribution of migration in Austria (postal code level) and US metropolitan areas, yielding precise estimates of migrant inflow at various geographic scales. Given the increase in international migrations due to recent natural and societal crises, this study enlightens our understanding of migration flow heterogeneities, helping design more inclusive, integrated cities.
... The development of epidemic models has gradually shifted from the traditional uniform mixing hypothesis to the networkstructured contact mode, with many different theoretical modeling approaches used [5][6][7][8][9][10]. Currently, human spatial activities are more frequent than ever [11], necessitating the description of individual migration characteristics using a metapopulation network model. This framework distributes individuals into different subpopulations (patches), with each individual corresponding to a particular patch representing realworld areas such as airports, cities, and meeting rooms. ...
The implementation of intervention strategies plays a crucial role in preventing the spread of epidemics. In this study, we incorporated travel restrictions and detection measures into a metapopulation framework, creating an epidemic model that takes into account recurrent mobility patterns. We determined theoretical thresholds subject to two constraints and executed comprehensive simulations to validate the model, which is founded upon Markovian equations. Our empirical findings reveal that enhancing detection efficiency, implementing rigorous travel restrictions, and reducing the transmission rate among regulated groups can effectively curtail the ultimate spread of the epidemic. However, we observed that the influence of travel restrictions weakens as the network’s average degree increases. Additionally, reducing the restriction parameter does not yield a higher threshold compared to when the mobility rate is low, as seen from a threshold perspective.
... By using data, policies and services can be enhanced and personalized to guarantee a better experience [Hess et al. 2015]. For example, when it comes to human mobility specifically, issues such as traffic flow prediction, contagion models, network resource optimization, urban planning, social behavior analysis and even migratory flows can be dealt with data collected at scale from multiple sources [Barbosa et al. 2018]. Another approach, a more traditional one, is done through the construction of mathematical and statistical models to derive the behaviors of the entities being studied with a certain degree of realism. ...
... Human behaviors are then recognized as a major factor affecting the epidemic trajectories on local and global scales [5][6][7][8] . Among the diverse behaviors, human mobility is key to disease spreading 9 . From the daily short-term commuters in large metropolitan areas to long-range travel via flights, trains, and buses, human mobility 10-12 plays a key role in shaping the spatiotemporal patterns of global epidemics [13][14][15] . ...
In our modern time, travel has become one of the most significant factors contributing to global epidemic spreading. A deficiency in the literature is that travel has largely been treated as a Markovian process: it occurs instantaneously without any memory effect. To provide informed policies such as determining the mandatory quarantine time, the non-Markovian nature of real-world traveling must be taken into account. We address this fundamental problem by constructing a network model in which travel takes a finite time and infections can occur during the travel. We find that the epidemic threshold can be maximized by a proper level of travel, implying that travel infections do not necessarily promote spreading. More importantly, the epidemic threshold can exhibit a two-threshold phenomenon in that it can increase abruptly and significantly as the travel time exceeds a critical value. This may provide a quantitative estimation of the minimally required quarantine time in a pandemic.
... To answer the first question, four general approaches to estimating mobility patterns of outdoor activities were identified in the literature (Barbosa et al. 2018;Alessandretti et al. 2020;Huang et al. 2018): (1) Direct measurement, which utilizes portable GPS sensors to monitor human mobility (Tian et al. 2020;Li et al. 2019); (2) Microenvironment models, which consider the number of people moved at specific locations during certain time window, such as AirGIS (Khan et al. 2019) and STEMS (Space-Time Exposure Modeling System) (Gulliver and Briggs 2005); (3) Empirical estimation, which uses existing datasets to estimate human mobility patterns, such as calling data records (Gariazzo et al. 2016), Google location history data (Ruktanonchai et al. 2018), travel history survey (Pindolia et al. 2012), and designed questionnaire (Soga et al. 2015); (4) Crowdsourced data models, which are contributed by users who turn to internet communities to answer research, survey, or feedback questions. The crowdsourced data models can obtain more general observations than traditional data conveniently, inexpensively, and relatively quickly. ...
Physical activity could improve individual health and reduce the risk of all-cause mortality. However, for health-promoting urban environments, some questions require further exploration. For instance, how urban form facilitates or constrains outdoor activities? How local meteorology modulates the urban form-outdoor exercise relationship? In this study, we apply a crowdsourced database, Strava, for outdoor exercisers in Atlanta, Georgia, to investigate the synergic effects of meteorological factors on the urban form-outdoor exercise relationship by developing two groups of models; one considers wind factors, and the other does not. The results show that the wind-related group outperforms their counterparts, especially for commute exercisers (R² = 0.77 vs. R² = 0.39), males (R² = 0.51 vs. R² = 0.39), and age groups of 13–19 (R² = 0.61 vs. R² = 0.25), demonstrating that incorporating local meteorological factors into urban form modeling can better reveal outdoor activity patterns. Besides, the urban form could impact the location preferences of individual exercisers, and such impact varies among different subgroups (e.g., seniors consider convenience, safety, and comfort more than young exercisers do). In addition, places become attractive for outdoor exercisers only when multiple urban form requirements are met (e.g., accessibility to public parks and proximity to residential communities). Finally, according to the non-monotonic and marginal effects, the impacts of urban form and meteorological factors on trip volume are only evident within specific ranges. These findings could help decision-makers make informed plans to promote more active and healthier communities.
... The gravity model (38) changed how population movements were predicted when it was first introduced in 1946 (100). Since then, it has become the most widespread model in this field (22). Despite this widespread use and popularity in the past, it is far from perfect. ...
This dissertation presents a comprehensive study on the design, analysis, and feasibility of a home delivery logistics network (HDLN) to enhance the efficiency of package delivery in urban and suburban areas. It compares the proximity factor with the traditional gravity model to predict interactions between distribution centers (DCs) and demand points, highlighting the robustness and reliability of the proximity factor. An algorithm for urban last-mile delivery is introduced, incorporating five critical parameters and a novel approach to calculate maximum delivery time using the population density. Furthermore, an expansion model integrates population forecasting and cost analysis to evaluate the feasibility of implementing the HDLN system. The findings demonstrate the effectiveness of the HDLN approach in achieving optimal utilization levels of DCs and covering a wide range of demand points within the region. Bottlenecks related to demand levels and DC capacity are identified, emphasizing the need for strategic planning. The research suggests future improvements, including enhancing the proximity factor algorithm to incorporate additional factors and testing its applicability in different scenarios. Furthermore, it proposes the utilization of varying DC sizes based on neighborhood characteristics and explores the potential expansion of HDLN at a national level. The inclusion of local road networks and the development of more sophisticated forecasting methods are also recommended for further research.
Reactionary delays have a large impact in the air transportation system both at operational and economical point of view. However, research efforts to understand their origin and propagation patterns in Europe have been limited. The TREE project (data-driven modeling of network-wide extension of the tree of reactionary delays in ECAC area) is focused on characterizing and forecasting the propagation of reactionary delays through the European Network. The best approach to tackle this problem passes through the use of Complex Systems theory. This theory analyzes systems formed by a large number of components interacting between them by means of networks and attempts at predicting their meso-scale and global behaviors. In this model, airports are taken as nodes and links between them are created by direct flights, with delays appearing as malfunctions in the daily planned schedule that can and do propagate over an important fraction of the network. An agent-based, data-driven approach is introduced, able to simulate the delay propagation process. The first results show a promising similarity with the real delay propagation patterns, being able to describe the cluster of congested airports and its evolution along the day.
Discrete choice analysis has become an industry standard in land use and transportation models. Such models are fundamentally grounded in individual choice; therefore, the treatment of interdependencies among decision makers is a formidable challenge. Through an empirical application to mode choice, the capture of interdependencies in discrete choice is described and illustrated. Decision makers are assumed to be influenced, for example, by people of similar socioeconomic status who are nearby. Given such social and spatial network relationships, the choice model captures interdependencies in two ways: (a) including in the systematic utility variables that describe choices of others in the decision maker's social and spatial network and (b) allowing for correlation across the disturbances of decision makers within the same social and spatial network. Variations of these approaches (including their combination and the use of random parameters) are tested with mode choice and compared with traditional methods of market segmentation. The application results indicate that the proposed methods for capturing interdependencies are significant and superior to traditional methods. Furthermore, capturing the interdependencies in the systematic utility is sufficient: it is better than the model with just correlation and is not significantly worse than the model with both the systematic term and correlation. The systematic term also captures a feedback effect that can propel the adoption of a new mode over time, for example. Models are estimated with the use of a traditional transportation data set and readily available software.
The city is a complex system that evolves through its inherent social and economic interactions. Mediating the movements of people and resources, urban street networks offer a spatial footprint of these activities; consequently their structural characteristics have been of great interest in the literature. In comparison, relatively limited attention has been devoted to the interplay between street structure and its functional usage, i.e., the movement patterns of people and resources. To address this, we study the shape of 472,040 spatiotemporally optimized travel routes in the 92 most populated cities in the world. The routes are sampled in a geographically unbiased way such that their properties can be mapped on to each city, with their summary statistics capturing mesoscale connectivity patterns representing the complete space of possible movement in cities. The collective morphology of routes exhibits a directional bias that could be described as influenced by the attractive (or repulsive) forces resulting from congestion, accessibility and travel demand that relate to various socioeconomic factors. To capture this feature, we propose a simple metric, inness, that maps this force field. An analysis of the morphological patterns of individual cities reveals structural and socioeconomic commonalities among cities with similar inness patterns, in particular, that they cluster into groups that are correlated with their size and putative stage of urban development as measured by a series of socioeconomic and infrastructural indicators. Our results lend weight to the insight that levels of urban socioeconomic development are intrinsically tied to increasing physical connectivity and diversity of road hierarchies.
The development of modeling systems for activity-based travel demand ushers in a new era in transportation demand forecasting and planning. A comprehensive multimodal activity-based system for forecasting travel demand was developed for implementation in Florida and resulted in the Florida Activity Mobility Simulator (FAMOS). Two main modules compose the FAMOS microsimulation model system for modeling activity–travel patterns of individuals: the Household Attributes Generation System and the Prism-Constrained Activity–Travel Simulator. FAMOS was developed and estimated with household activity and travel data collected in southeast Florida in 2000. Results of the model development effort are promising and demonstrate the applicability of activity-based model systems in travel demand forecasting. An overview of the model system, a description of its features and capabilities, and preliminary validation results are provided.
In the last two decades it has become increasingly clear that the spatial dimension is a critically important aspect of ecological dynamics. Ecologists are currently investing an enormous amount of effort in quantifying movement patterns of organisms. Connecting these data to general issues in metapopulation biology and landscape ecology, as well as to applied questions in conservation and natural resource management, however, has proved to be a non-trivial task. This book presents a systematic exposition of quantitative methods for analyzing and modeling movements of organisms in the field. Quantitative Analysis of Movement is intended for graduate students and researchers interested in spatial ecology, including applications to conservation, pest control, and fisheries. Models are a key ingredient in the analytical approaches developed in the book; however, the primary focus is not on mathematical methods, but on connections between models and data. The methodological approaches discussed in the book will be useful to ecologists working with all taxonomic groups. Case studies have been selected from a wide variety of organisms, including plants (seed dispersal, spatial spread of clonal plants), insects, and vertebrates (primarily, fish, birds, and mammals).
To be able to curb the global pandemic of physical inactivity and the associated 5.3 million deaths per year, we need to understand the basic principles that govern physical activity. However, there is a lack of large-scale measurements of physical activity patterns across free-living populations worldwide. Here we leverage the wide usage of smartphones with built-in accelerometry to measure physical activity at the global scale. We study a dataset consisting of 68 million days of physical activity for 717,527 people, giving us a window into activity in 111 countries across the globe. We find inequality in how activity is distributed within countries and that this inequality is a better predictor of obesity prevalence in the population than average activity volume. Reduced activity in females contributes to a large portion of the observed activity inequality. Aspects of the built environment, such as the walkability of a city, are associated with a smaller gender gap in activity and lower activity inequality. In more walkable cities, activity is greater throughout the day and throughout the week, across age, gender, and body mass index (BMI) groups, with the greatest increases in activity found for females. Our findings have implications for global public health policy and urban planning and highlight the role of activity inequality and the built environment in improving physical activity and health.
Recent developments of the multilayer paradigm include efforts to understand the role played by the presence of several layers on the dynamics of processes running on the networks. The possible existence of new phenomena associated to the richer multilayer topology has been discussed and examples of these differences have been systematically searched for. Here, we show that the interconnectivity of the layers may have an important impact on the speed of the dynamics run in the network and that microscopic changes such as the addition of one single inter-layer link can notably affect the arrival at a global stationary state. As a practical testbed, these results obtained with spectral techniques are confirmed with a Kuramoto dynamics for which the synchronization consistently accelerates after the addition of single inter-layer links.