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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.
... Trajectory data [3] captures frequently measured location sequences of objects such as humans [26], vehicles [22], and animals [6]. Trajectory data enables a deeper analysis of human behaviors [21,27], their mobility [17,18], enhancing infectious disease modeling [12], and facilitating urban mobility studies [5,8,28]. Additionally, it is instrumental in traffic analysis [7], the detection of anomalies [2,13,23,24] and urban planning [9,25]. ...
The emergence of SARS-CoV-2 into a highly susceptible global population was primarily driven by human mobility-induced introduction events. Especially in the early stages, understanding mobility was vital to mitigating the pandemic prior to widespread vaccine availability. We conducted a systematic review of studies published from January 1, 2020, to May 9, 2021, that used population-level human mobility data to understand SARS-CoV-2 transmission. Of the 5505 papers with abstracts screened, 232 were included in the analysis. These papers focused on a range of specific questions but were dominated by analyses focusing on the USA and China. The majority included mobile phone data, followed by Google Community Mobility Reports, and few included any adjustments to account for potential biases in population sampling processes. There was no clear relationship between methods used to integrate mobility and SARS-CoV-2 data and goals of analysis. When considering papers focused only on the estimation of the effective reproductive number within the US, there was no clear relationship identified between this measure and changes in mobility patterns. Our findings underscore the need for standardized, systematic ways to identify the source of mobility data, select an appropriate approach to using it in analysis, and reporting.
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.
