The availability of massive digital traces of human whereabouts has offered a series of novel insights on the quantitative patterns characterizing human mobility. In particular, numerous recent studies have lead to an unexpected consensus: the considerable variability in the characteristic travelled distance of individuals coexists with a high degree of predictability of their future locations. Here we shed light on this surprising coexistence by systematically investigating the impact of recurrent mobility on the characteristic distance travelled by individuals. Using both mobile phone and GPS data, we discover the existence of two distinct classes of individuals: returners and explorers. As existing models of human mobility cannot explain the existence of these two classes, we develop more realistic models able to capture the empirical findings. Finally, we show that returners and explorers play a distinct quantifiable role in spreading phenomena and that a correlation exists between their mobility patterns and social interactions.
... 1. Various distribution functions and parameter setting methods were used to model the distribution of waiting times in the EPR model. For example, some studies fitted the waiting-time distribution based on trajectory data from metropolitan or global areas (Barbosa et al. 2015); whereas others applied the truncated powerlaw distribution and inherited the parameters from previous literature (Pappalardo et al. 2015) or directly set the parameters by themselves (Wang et al. 2019). ...
... Gravity-based models (Zipf 1946, Noulas et al. 2012, Wang et al. 2019) and intervening opportunity-based models (Stouffer 1940, Simini et al. 2012, Yan et al. 2014, Liu and Yan 2019, Liu and Yan 2020 are commonly used to select the next location in the exploration phase. Some studies have directly used the gravity model with a set of fixed parameters (Pappalardo et al. 2015, Pappalardo andSimini 2018), without testing the suitability of the model and parameters for the specific mobility data and urban areas. Moreover, the gravity model is the most classic but not the most accurate. ...
... � Continuous Time Random Walk (CTRW) (Brockmann et al. 2006) applied the truncated power-law distribution functions for both waiting time Dt and jump length Dr: here, a ¼ 0:5960:02; b ¼ 0:6060:03: In line with the study by Pappalardo et al. (2015), we substituted the sampling of jump length from P Dr ð Þ with a distancebased location choice model during implementation: ...
... To analyze it, we extract eight statistical features of mobility patterns of an individual traveler, including TD avg , R IVL , radius of gyration (r g ), top N r g , and four spatiotemporalrelated entropies. r g highlights spatial range of traveling activities, and top N r g is defined by focusing only the N most frequently visited places to emphasize transitions among them (Pappalardo et al. 2015). Specifically, we set the N as 2, taking residential and workplace as two anchor points in traveling. ...
Individual mobility prediction forecasts traveling activities of an individual traveler, and has wide applications in location-based services, public health, and transportation planning. Whereas, it remains challenging due to the complexity and uncertainty of human mobility. Existing methods mainly consider spatiotemporal contexts in current traveling, but overlook those in historical trips, as well as relationships between traversed road intersections. These issues hinder the model from effectively capturing complex mobility patterns. To fill this gap, we propose a novel method that incorporates current traveling features and historical activity chain to predict the coordinates of traveling destination. Specifically, (1) we construct current traveling features by extracting real-time moving states, and represent spatiotemporal correlations between traversed road intersections using word embedding; (2) we learn travel intentions as a probability vector for each historical trip, and combine it with spatiotemporal features to construct historical activity chain; (3) we construct an individual mobility prediction model using Long Short-Term Memory (LSTM) network and spatiotemporal scoring mechanism, to capture short-term and long-term dependencies in current trip and historical activity chain, respectively. Experiments on 21,890 trajectories over the whole Year 2019 of 20 representatives selected from 1916 private car travelers in Shenzhen City, reveal the effectiveness of our model. It outperforms four baselines, Random Forest (RF), Distant Neighboring Dependencies (DND), Location Semantics and Location Importance (LSI)-LSTM, as well as Intersection Transfer Preference and Current Movement Mode (ITP-CMM), by approximately 10%-15% improvement in accuracy. In addition, we further explore the impact of historical activity chain length, and destination visiting frequency on prediction, as well as the relationship between predictability and eight mobility pattern features. This study benefits potential applications such as personalized location-based service recommendations and targeted advertising, and also provides implications for understanding human mobility.
... The gravity model, as a classical model for exploring migration, focuses on the factor of distance as a core factor that affects the choice of migration destination. Consequently, distance has always been recognized as a vital element in theories of migration (Pappalardo et al., 2015;Simini et al., 2012). In the early days of sociological research, Zipf (1946) proposed a gravity model for predicting migration. ...
At the global scale, cross-border migration as well as intra-country migration is common. As an individual-level adaptive strategy, migration has an important impact on economic development, socio-political and cultural process. However, scholars have mostly focused on spatial-temporal variations, predictions about migration using models and individuals’ behavior at the micro-level, only limiting researches that analyze migration from a multidimensional perspective. Therefore, in this paper, we focus on China’s floating population, empirically reveal the spatial distribution trend of migration and its influence mechanisms by constructing a multidimensional distances model. Results show that (1) The net migrating population shows a community structure, with low-value area’s agglomeration, and a multi-polar distribution in high-value areas, and some provincial capitals experiencing an obvious ‘population filling’ phenomenon. The migration flows are mainly from prefecture-level cities to capitals of the same province, and from neighboring cities to megacities such as Beijing, Shanghai, Tianjin, etc. (2) After controlling the characteristic factors of the out-migrating place and the destination city, economic distance significantly promotes migration flows, while geographic, administrative and cultural distance hinders migration. (3) In different regions, there are significant differences in the mechanisms by which multidimensional distances affect migration. The study has constructed a relatively complete and unified analytical framework for the impact of multidimensional distances on migration, which can help enrich the study of migration in geography and provide scientific references for relevant policy formulations.
... When analyzed at an individual level, human mobility provides more detailed insights by accounting for personal differences. Research shows that individuals exhibit varied behaviors in their movement patterns [18,21] with a recent study [3] identifying three mobility profiles (i) Scouters, more inclined to explore and discover new areas; (ii) Routiners, who maintain a steady routine and rarely break their established patterns; and (iii) Regulars, with a moderate behavior balancing between explorations and revisits. ...
Mobile devices have become essential for capturing human activity, and eXtended Data Records (XDRs) offer rich opportunities for detailed user behavior modeling, which is useful for designing personalized digital services. Previous studies have primarily focused on aggregated mobile traffic and mobility analyses, often neglecting individual-level insights. This paper introduces a novel approach that explores the dependency between traffic and mobility behaviors at the user level. By analyzing 13 individual features that encompass traffic patterns and various mobility aspects, we enhance the understanding of how these behaviors interact. Our advanced user modeling framework integrates traffic and mobility behaviors over time, allowing for fine-grained dependencies while maintaining population heterogeneity through user-specific signatures. Furthermore, we develop a Markov model that infers traffic behavior from mobility and vice versa, prioritizing significant dependencies while addressing privacy concerns. Using a week-long XDR dataset from 1,337,719 users across several provinces in Chile, we validate our approach, demonstrating its robustness and applicability in accurately inferring user behavior and matching mobility and traffic profiles across diverse urban contexts.
... In addition, small retail stores should endeavour to periodically sharpen EC to improve upon their creativity, thereby innovating more which in the long run will affect the performance of their firms. Once OCB directly affects EIWB and weakens the relationship between PDM and EIWB, small retailers should take a second look at OCB and discuss this anomaly with their workers to avert the possibility of the workers ending this behaviour abruptly since human behaviour is not predictable (Pappalardo et al., 2015). Also, deeper interactions with workers and suggestions from both parties could clear this hurdle. ...
The study aims to examine the effects of participatory decision-making (PDM) on the innovation of workers of small retail stores in Kumasi, Ghana. It is also to analyse the mediating role of job satisfaction (JS) and the moderating roles of both organisational citizenship behaviour (OCB) and employee competencies (EC) between PDM and employees’ innovative work (EIWB). Based on the social exchange theory (SET), a sample of 723 workers from small retail stores is used for the study. The Partial Least Squares (PLS) and Structural Equation Modelling (SEM) are also adopted to test eight hypotheses. The research highlights the five variable relationships entailed in the model and therefore confirms SET in the Ghanaian retail industry. It is found that PDM has a positive link with JS and EIWB while PDM has an indirect connection with EIWB through JS. Also, the association between PDM and EIWB is strengthened by EC and weakened by OCB. Again, EIWB is positively influenced by JS, OCB and EC. The findings throw light on how policymakers and practitioners can improve EIWB in the retail industry.
... Mobility models offer researchers a detailed examination of the features influencing mobility and segregation patterns. A classic model is Exploration and Preferential Return (EPR) [46,53], which uses two key parameters, exploration (visiting new places) and preferential return (revisiting previously visited locations), to model the dynamics of human movement trajectories. Moro et al. [42] extend the EPR by adding a parameter that quantifies an individual motivation to visit new places where their income group is in the minority. ...
Urban segregation refers to the physical and social division of people, often driving inequalities within cities and exacerbating socioeconomic and racial tensions. While most studies focus on residential spaces, they often neglect segregation across "activity spaces" where people work, socialize, and engage in leisure. Human mobility data offers new opportunities to analyze broader segregation patterns, encompassing both residential and activity spaces, but challenges existing methods in capturing the complexity and local nuances of urban segregation. This work introduces InclusiViz, a novel visual analytics system for multi-level analysis of urban segregation, facilitating the development of targeted, data-driven interventions. Specifically, we developed a deep learning model to predict mobility patterns across social groups using environmental features, augmented with explainable AI to reveal how these features influence segregation. The system integrates innovative visualizations that allow users to explore segregation patterns from broad overviews to fine-grained detail and evaluate urban planning interventions with real-time feedback. We conducted a quantitative evaluation to validate the model's accuracy and efficiency. Two case studies and expert interviews with social scientists and urban analysts demonstrated the system's effectiveness, highlighting its potential to guide urban planning toward more inclusive cities.
... With advancements in transportation and the widespread use of smart devices, people increasingly rely on public transportation and smart devices for their daily lives and work. This shift generates vast amounts of historical travel data, including GPS trajectories [1,2], mobile phone signal records [3], cellular network signaling data [4], as well as bus [5] and metro ticket records [6]. These data sets provide invaluable insights into the regularities of population movements and patterns of human mobility, capturing the spatiotemporal dynamics of individuals and groups. ...
Human mobility varies significantly across temporal and spatial scales and exhibits distinct characteristics. However, precise methods and metrics for measuring human mobility are still lacking and the underlying mechanisms across different spatiotemporal scales and social groups remain unexplored. To uncover the regularity of urban travel patterns, we propose statistical methods focused on different types of entropy values. We introduce the concepts of mobility chains and travel motifs to provide diversified perspectives to observing users' travel behaviors, choices, preferences and other characteristics. Our findings reveal that users with the same travel motifs share similar proportions in each city and follow consistent travel regularities within their motif categories. Given the importance of understanding human mobility, we emphasize the need for quantitative models that account for the statistical characteristics of individual human trajectories. To address this, we introduce the Preferential Return (PR) model, which explains the observed scaling laws and analytically simulates users' travel behaviors. Our model and associated rules establish an underlying mechanism at the individual level, capable of explaining a variety of human mobility behaviors with different travel characteristics. These analyses and explanations have significant applications in reproducing human movement patterns. Our study provides a scientific basis for understanding and optimizing urban traffic management, enhancing public service efficiency, and promoting sustainable urban development. We believe these insights will contribute to the harmonious progress of society.
The timely, accurate monitoring of social indicators, such as poverty or inequality, on a finegrained spatial and temporal scale is a crucial tool for understanding social phenomena and policymaking, but poses a great challenge to official statistics. This article argues that an interdisciplinary approach, combining the body of statistical research in small area estimation with the body of research in social data mining based on Big Data, can provide novel means to tackle this problem successfully. Big Data derived from the digital crumbs that humans leave behind in their daily activities are in fact providing ever more accurate proxies of social life. Social data mining from these data, coupled with advanced model-based techniques for fine-grained estimates, have the potential to provide a novel microscope through which to view and understand social complexity. This article suggests three ways to use Big Data together with small area estimation techniques, and shows how Big Data has the potential to mirror aspects of well-being and other socioeconomic phenomena.
This study leverages mobile phone data to analyze human mobility patterns in a developing nation, especially in comparison to those of a more industrialized nation. Developing regions, such as the Ivory Coast, are marked by a number of factors that may influence mobility, such as less infrastructural coverage and maturity, less economic resources and stability, and in some cases, more cultural and language-based diversity. By comparing mobile phone data collected from the Ivory Coast to similar data collected in Portugal, we are able to highlight both qualitative and quantitative differences in mobility patterns - such as differences in likelihood to travel, as well as in the time required to travel - that are relevant to consideration on policy, infrastructure, and economic development. Our study illustrates how cultural and linguistic diversity in developing regions (such as Ivory Coast) can present challenges to mobility models that perform well and were conceptualized in less culturally diverse regions. Finally, we address these challenges by proposing novel techniques to assess the strength of borders in a regional partitioning scheme and to quantify the impact of border strength on mobility model accuracy.
This study leverages mobile phone data to analyze human mobility patterns in
developing countries, especially in comparison to more industrialized
countries. Developing regions, such as the Ivory Coast, are marked by a number
of factors that may influence mobility, such as less infrastructural coverage
and maturity, less economic resources and stability, and in some cases, more
cultural and language-based diversity. By comparing mobile phone data collected
from the Ivory Coast to similar data collected in Portugal, we are able to
highlight both qualitative and quantitative differences in mobility patterns -
such as differences in likelihood to travel, as well as in the time required to
travel - that are relevant to consideration on policy, infrastructure, and
economic development. Our study illustrates how cultural and linguistic
diversity in developing regions (such as Ivory Coast) can present challenges to
mobility models that perform well and were conceptualized in less culturally
diverse regions. Finally, we address these challenges by proposing novel
techniques to assess the strength of borders in a regional partitioning scheme
and to quantify the impact of border strength on mobility model accuracy.
A proposal for a new way to understand cities and their design not as artifacts but as systems composed of flows and networks.
In The New Science of Cities, Michael Batty suggests that to understand cities we must view them not simply as places in space but as systems of networks and flows. To understand space, he argues, we must understand flows, and to understand flows, we must understand networks—the relations between objects that compose the system of the city. Drawing on the complexity sciences, social physics, urban economics, transportation theory, regional science, and urban geography, and building on his own previous work, Batty introduces theories and methods that reveal the deep structure of how cities function.
Batty presents the foundations of a new science of cities, defining flows and their networks and introducing tools that can be applied to understanding different aspects of city structure. He examines the size of cities, their internal order, the transport routes that define them, and the locations that fix these networks. He introduces methods of simulation that range from simple stochastic models to bottom-up evolutionary models to aggregate land-use transportation models. Then, using largely the same tools, he presents design and decision-making models that predict interactions and flows in future cities. These networks emphasize a notion with relevance for future research and planning: that design of cities is collective action.
Given its effective techniques and theories from various sources and fields, data science is playing a vital role in transportation research and the consequences of the inevitable switch to electronic vehicles. This fundamental insight provides a step towards the solution of this important challenge.
Data Science and Simulation in Transportation Research highlights entirely new and detailed spatial-temporal micro-simulation methodologies for human mobility and the emerging dynamics of our society. Bringing together novel ideas grounded in big data from various data mining and transportation science sources, this book is an essential tool for professionals, students, and researchers in the fields of transportation research and data mining.
The potential of low-frequency bus localization data for the monitoring and control of bus system performance is investigated in this paper. It is shown that data with a sampling rate as low as 1 min, when processed appropriately, can provide ample information. Accurate estimates of stop arrival and departure times are obtained; these estimates in turn allow the analysis of headways and travel times. A three-parameter gamma family of distributions is fitted for headways at the stops along a bus line. The evolution of the parameters demonstrates critical points on the line where bus bunching is significantly increased. Moreover, this analysis allows differentiating problems associated with varying passenger demand from uncertainties associated with traffic conditions. Furthermore it is shown that expected travel time and travel time variability can be calculated from low-frequency localization data. Finally, the way in which the results can be used to calibrate a simulation model that can test bus control strategies is presented. The methods are applied and validated to data obtained from Bus Route Number 1 in Boston, Massachusetts.