A survey on Human Mobility and its applications
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paper_title: Path lengths, correlations, and centrality in temporal networks

paper_content:

In temporal networks, where nodes interact via sequences of temporary events, information or resources can only flow through paths that follow the time ordering of events. Such temporal paths play a crucial role in dynamic processes. However, since networks have so far been usually considered static or quasistatic, the properties of temporal paths are not yet well understood. Building on a definition and algorithmic implementation of the average temporal distance between nodes, we study temporal paths in empirical networks of human communication and air transport. Although temporal distances correlate with static graph distances, there is a large spread, and nodes that appear close from the static network view may be connected via slow paths or not at all. Differences between static and temporal properties are further highlighted in studies of the temporal closeness centrality. In addition, correlations and heterogeneities in the underlying event sequences affect temporal path lengths, increasing temporal distances in communication networks and decreasing them in the air transport network.
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paper_title: Optimal Spatial Resolution for the Analysis of Human Mobility

paper_content:

The availability of massive network and mobility data from diverse domains has fostered the analysis of human behaviors and interactions. This data availability leads to challenges in the knowledge discovery community. Several different analyses have been performed on the traces of human trajectories, such as understanding the real borders of human mobility or mining social interactions derived from mobility and vice versa. However, the data quality of the digital traces of human mobility has a dramatic impact over the knowledge that it is possible to mine, and this issue has not been thoroughly tackled so far in literature. In this paper, we mine and analyze with complex network techniques a large dataset of human trajectories, a GPS dataset from more than 150k vehicles in Italy. We build a multi resolution grid and we map the trajectories with several complex networks, by connecting the different areas of our region of interest. Then we analyze the structural properties of these networks and the quality of the borders it is possible to infer from them. The result is a significant advancement in our understanding of the data transformation process that is needed to connect mobility with social network analysis and mining.
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paper_title: Discovering regions of different functions in a city using human mobility and POIs

paper_content:

The development of a city gradually fosters different functional regions, such as educational areas and business districts. In this paper, we propose a framework (titled DRoF) that Discovers Regions of different Functions in a city using both human mobility among regions and points of interests (POIs) located in a region. Specifically, we segment a city into disjointed regions according to major roads, such as highways and urban express ways. We infer the functions of each region using a topic-based inference model, which regards a region as a document, a function as a topic, categories of POIs (e.g., restaurants and shopping malls) as metadata (like authors, affiliations, and key words), and human mobility patterns (when people reach/leave a region and where people come from and leave for) as words. As a result, a region is represented by a distribution of functions, and a function is featured by a distribution of mobility patterns. We further identify the intensity of each function in different locations. The results generated by our framework can benefit a variety of applications, including urban planning, location choosing for a business, and social recommendations. We evaluated our method using large-scale and real-world datasets, consisting of two POI datasets of Beijing (in 2010 and 2011) and two 3-month GPS trajectory datasets (representing human mobility) generated by over 12,000 taxicabs in Beijing in 2010 and 2011 respectively. The results justify the advantages of our approach over baseline methods solely using POIs or human mobility.
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paper_title: Path lengths, correlations, and centrality in temporal networks

paper_content:

In temporal networks, where nodes interact via sequences of temporary events, information or resources can only flow through paths that follow the time ordering of events. Such temporal paths play a crucial role in dynamic processes. However, since networks have so far been usually considered static or quasistatic, the properties of temporal paths are not yet well understood. Building on a definition and algorithmic implementation of the average temporal distance between nodes, we study temporal paths in empirical networks of human communication and air transport. Although temporal distances correlate with static graph distances, there is a large spread, and nodes that appear close from the static network view may be connected via slow paths or not at all. Differences between static and temporal properties are further highlighted in studies of the temporal closeness centrality. In addition, correlations and heterogeneities in the underlying event sequences affect temporal path lengths, increasing temporal distances in communication networks and decreasing them in the air transport network.
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paper_title: Human mobility models and opportunistic communications system design

paper_content:

In this paper, we seek to improve understanding of the structure of human mobility, with a view to using this for designing algorithms for the dissemination of data among mobile users. We analyse community structures and node centrality from the human mobility traces and use these two metrics to design efficient forwarding algorithms in terms of delivery ratio and delivery cost for mobile networks. This is the first empirical study of community and centrality using real human mobility datasets.
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paper_title: Diversity of Individual Mobility Patterns

paper_content:

Uncovering human mobility patterns is of fundamental importance to the understanding of epidemic spreading, urban transportation and other socioeconomic dynamics embodying spatiality and human travel. The observed scaling laws for aggregated data require a theoretical explanation of their underlying mechanism. According to the direct travel diaries of volunteers, we show the absence of scaling properties in the displacement distribution at the individual level, which unfortunately provides a complete contrast to most inferences and assumptions in the literature. The aggregated displacement distribution follows a power law with an exponential cutoff, which is analytically explained by the mixture nature of human travel under Maxwell-Boltzmann statistics. Our analysis provides an alternative way to bridge diverse patterns at the individual level and scaling laws at the population level.
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paper_title: A two-scale network dynamic model for human mobility process.

paper_content:

The technological changes and educational expansion have created the heterogeneity in the human species. Clearly, this heterogeneity generates a structure in the population dynamics, namely: citizen, permanent resident, visitor, and etc. Furthermore, as the heterogeneity in the population increases, the human mobility between meta-populations patches also increases. Depending on spatial scales, a meta-population patch can be decomposed into sub-patches, for examples: homes, neighborhoods, towns, etc. Members of the population can move between the sub-patches. The dynamics of human mobility in a heterogeneous and scaled structured population is still its infancy level. In this work, an attempt is made to investigate the human mobility dynamics of heterogeneous and scaled structured population. We present a two scaled human mobility model for a meta-population. The sub regions and regions are interlinked via intra-and inter regional transport network systems. Under various types of growth order assumptions on the intra and interregional residence times of the residents of a sub region, different patterns of static behavior of the mobility process is studied. In addition, the results reveal that the system has a natural tendency to quarantine itself without total breaking a link in the transportation network system. Moreover, there is a threshold point for the largest intra regional visiting time of residents of a given sub region that leads to either a total isolation of the residents from other sub regions within the region or a partial isolation of residents from some of the sub regions within the region.
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paper_title: Calling patterns in human communication dynamics

paper_content:

Modern technologies not only provide a variety of communication modes (e.g., texting, cell phone conversation, and online instant messaging), but also detailed electronic traces of these communications between individuals. These electronic traces indicate that the interactions occur in temporal bursts. Here, we study intercall duration of communications of the 100,000 most active cell phone users of a Chinese mobile phone operator. We confirm that the intercall durations follow a power-law distribution with an exponential cutoff at the population level but find differences when focusing on individual users. We apply statistical tests at the individual level and find that the intercall durations follow a power-law distribution for only 3,460 individuals (3.46%). The intercall durations for the majority (73.34%) follow a Weibull distribution. We quantify individual users using three measures: out-degree, percentage of outgoing calls, and communication diversity. We find that the cell phone users with a power-law duration distribution fall into three anomalous clusters: robot-based callers, telecom fraud, and telephone sales. This information is of interest to both academics and practitioners, mobile telecom operators in particular. In contrast, the individual users with a Weibull duration distribution form the fourth cluster of ordinary cell phone users. We also discover more information about the calling patterns of these four clusters (e.g., the probability that a user will call the cr-th most contact and the probability distribution of burst sizes). Our findings may enable a more detailed analysis of the huge body of data contained in the logs of massive users.
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paper_title: The scaling laws of human travel

paper_content:

The website wheresgeorge.com invites its users to enter the serial numbers of their US dollar bills and track them across America and beyond. Why? “For fun and because it had not been done yet”, they say. But the dataset accumulated since December 1998 has provided the ideal raw material to test the mathematical laws underlying human travel, and that has important implications for the epidemiology of infectious diseases. Analysis of the trajectories of over half a million dollar bills shows that human dispersal is described by a ‘two-parameter continuous-time random walk’ model: our travel habits conform to a type of random proliferation known as ‘superdiffusion’. And with that much established, it should soon be possible to develop a new class of models to account for the spread of human disease. The dynamic spatial redistribution of individuals is a key driving force of various spatiotemporal phenomena on geographical scales. It can synchronize populations of interacting species, stabilize them, and diversify gene pools1,2,3. Human travel, for example, is responsible for the geographical spread of human infectious disease4,5,6,7,8,9. In the light of increasing international trade, intensified human mobility and the imminent threat of an influenza A epidemic10, the knowledge of dynamical and statistical properties of human travel is of fundamental importance. Despite its crucial role, a quantitative assessment of these properties on geographical scales remains elusive, and the assumption that humans disperse diffusively still prevails in models. Here we report on a solid and quantitative assessment of human travelling statistics by analysing the circulation of bank notes in the United States. Using a comprehensive data set of over a million individual displacements, we find that dispersal is anomalous in two ways. First, the distribution of travelling distances decays as a power law, indicating that trajectories of bank notes are reminiscent of scale-free random walks known as Levy flights. Second, the probability of remaining in a small, spatially confined region for a time T is dominated by algebraically long tails that attenuate the superdiffusive spread. We show that human travelling behaviour can be described mathematically on many spatiotemporal scales by a two-parameter continuous-time random walk model to a surprising accuracy, and conclude that human travel on geographical scales is an ambivalent and effectively superdiffusive process.
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paper_title: Characterising temporal distance and reachability in mobile and online social networks

paper_content:

The analysis of social and technological networks has attracted a lot of attention as social networking applications and mobile sensing devices have given us a wealth of real data. Classic studies looked at analysing static or aggregated networks, i.e., networks that do not change over time or built as the results of aggregation of information over a certain period of time. Given the soaring collections of measurements related to very large, real network traces, researchers are quickly starting to realise that connections are inherently varying over time and exhibit more dimensionality than static analysis can capture. In this paper we propose new temporal distance metrics to quantify and compare the speed (delay) of information diffusion processes taking into account the evolution of a network from a global view. We show how these metrics are able to capture the temporal characteristics of time-varying graphs, such as delay, duration and time order of contacts (interactions), compared to the metrics used in the past on static graphs. We also characterise network reachability with the concepts of in- and out-components. Then, we generalise them with a global perspective by defining temporal connected components. As a proof of concept we apply these techniques to two classes of time-varying networks, namely connectivity of mobile devices and interactions on an online social network.
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paper_title: Understanding individual human mobility patterns

paper_content:

Despite their importance for urban planning, traffic forecasting and the spread of biological and mobile viruses, our understanding of the basic laws governing human motion remains limited owing to the lack of tools to monitor the time-resolved location of individuals. Here we study the trajectory of 100,000 anonymized mobile phone users whose position is tracked for a six-month period. We find that, in contrast with the random trajectories predicted by the prevailing Lévy flight and random walk models, human trajectories show a high degree of temporal and spatial regularity, each individual being characterized by a time-independent characteristic travel distance and a significant probability to return to a few highly frequented locations. After correcting for differences in travel distances and the inherent anisotropy of each trajectory, the individual travel patterns collapse into a single spatial probability distribution, indicating that, despite the diversity of their travel history, humans follow simple reproducible patterns. This inherent similarity in travel patterns could impact all phenomena driven by human mobility, from epidemic prevention to emergency response, urban planning and agent-based modelling.
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paper_title: Temporal distance metrics for social network analysis

paper_content:

The analysis of social and technological networks has attracted a lot of attention as social networking applications and mobile sensing devices have given us a wealth of real data. Classic studies looked at analysing static or aggregated networks, i.e., networks that do not change over time or built as the results of aggregation of information over a certain period of time. Given the soaring collections of measurements related to very large, real network traces, researchers are quickly starting to realise that connections are inherently varying over time and exhibit more dimensionality than static analysis can capture. In this paper we propose new temporal distance metrics to quantify and compare the speed (delay) of information diffusion processes taking into account the evolution of a network from a local and global view. We show how these metrics are able to capture the temporal characteristics of time-varying graphs, such as delay, duration and time order of contacts (interactions), compared to the metrics used in the past on static graphs. As a proof of concept we apply these techniques to two classes of time-varying networks, namely connectivity of mobile devices and e-mail exchanges.
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paper_title: Communicability across evolving networks.

paper_content:

Many natural and technological applications generate time-ordered sequences of networks, defined over a fixed set of nodes; for example, time-stamped information about "who phoned who" or "who came into contact with who" arise naturally in studies of communication and the spread of disease. Concepts and algorithms for static networks do not immediately carry through to this dynamic setting. For example, suppose A and B interact in the morning, and then B and C interact in the afternoon. Information, or disease, may then pass from A to C, but not vice versa. This subtlety is lost if we simply summarize using the daily aggregate network given by the chain A-B-C. However, using a natural definition of a walk on an evolving network, we show that classic centrality measures from the static setting can be extended in a computationally convenient manner. In particular, communicability indices can be computed to summarize the ability of each node to broadcast and receive information. The computations involve basic operations in linear algebra, and the asymmetry caused by time's arrow is captured naturally through the noncommutativity of matrix-matrix multiplication. Illustrative examples are given for both synthetic and real-world communication data sets. We also discuss the use of the new centrality measures for real-time monitoring and prediction.
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paper_title: Persistence and periodicity in a dynamic proximity network

paper_content:

The topology of social networks can be understood as being inherently dynamic, with edges having a distinct position in time. Most characterizations of dynamic networks discretize time by converting temporal information into a sequence of network "snapshots" for further analysis. Here we study a highly resolved data set of a dynamic proximity network of 66 individuals. We show that the topology of this network evolves over a very broad distribution of time scales, that its behavior is characterized by strong periodicities driven by external calendar cycles, and that the conversion of inherently continuous-time data into a sequence of snapshots can produce highly biased estimates of network structure. We suggest that dynamic social networks exhibit a natural time scale \Delta_{nat}, and that the best conversion of such dynamic data to a discrete sequence of networks is done at this natural rate.
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paper_title: Spatiotemporal Data from Mobile Phones for Personal Mobility Assessment

paper_content:

Abstract ::: Purpose — In this chapter, we will review several alternative methods of collecting data from mobile phones for human mobility analysis. We propose considering cellular network location data as a useful complementary source for human mobility research and provide case studies to illustrate the advantages and disadvantages of each method. ::: ::: Methodology/approach — We briefly describe cellular phone network architecture and the location data it can provide, and discuss two types of data collection: active and passive localization. Active localization is something like a personal travel diary. It provides a tool for recording positioning data on a survey sample over a long period of time. Passive localization, on the other hand, is based on phone network data that are automatically recorded for technical or billing purposes. It offers the advantage of access to very large user populations for mobility flow analysis of a broad area. ::: ::: Findings — We review several alternative methods of collecting data from mobile phone for human mobility analysis to show that cellular network data, although limited in terms of location precision and recording frequency, offer two major advantages for studying human mobility. First, very large user samples – covering broad geographical areas – can be followed over a long period of time. Second, this type of data allows researchers to choose a specific data collection methodology (active or passive), depending on the objectives of their study. The big mobile phone localization datasets have provided a new impulse for the interdisciplinary research in human mobility. ::: ::: Originality/value of chapter — We propose considering cellular network location data as a useful complementary source for transportation research and provide case studies to illustrate the advantages and disadvantages of each proposed method. Mobile phones have become a kind of “personal sensor” offering an ever-increasing amount of location data on mobile phone users over long time periods. These data can thus provide a framework for a comprehensive and longitudinal study of temporal dynamics, and can be used to capture ephemeral events and fluctuations in day-to-day mobility behavior offering powerful tools to transportation research, urban planning, or even real-time city monitoring.
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paper_title: Analysing information flows and key mediators through temporal centrality metrics

paper_content:

The study of influential members of human networks is an important research question in social network analysis. However, the current state-of-the-art is based on static or aggregated representation of the network topology. We argue that dynamically evolving network topologies are inherent in many systems, including real online social and technological networks: fortunately the nature of these systems is such that they allow the gathering of large quantities of finegrained temporal data on interactions amongst the network members. In this paper we propose novel temporal centrality metrics which take into account such dynamic interactions over time. Using a real corporate email dataset we evaluate the important individuals selected by means of static and temporal analysis taking two perspectives: firstly, from a semantic level, we investigate their corporate role in the organisation; and secondly, from a dynamic process point of view, we measure information dissemination and the role of information mediators. We find that temporal analysis provides a better understanding of dynamic processes and a more accurate identification of important people compared to traditional static methods.
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paper_title: Human mobility modeling at metropolitan scales

paper_content:

Models of human mobility have broad applicability in fields such as mobile computing, urban planning, and ecology. This paper proposes and evaluates WHERE, a novel approach to modeling how large populations move within different metropolitan areas. WHERE takes as input spatial and temporal probability distributions drawn from empirical data, such as Call Detail Records (CDRs) from a cellular telephone network, and produces synthetic CDRs for a synthetic population. We have validated WHERE against billions of anonymous location samples for hundreds of thousands of phones in the New York and Los Angeles metropolitan areas. We found that WHERE offers significantly higher fidelity than other modeling approaches. For example, daily range of travel statistics fall within one mile of their true values, an improvement of more than 14 times over a Weighted Random Waypoint model. Our modeling techniques and synthetic CDRs can be applied to a wide range of problems while avoiding many of the privacy concerns surrounding real CDRs.
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paper_title: Human mobility characterization from cellular network data

paper_content:

Anonymous location data from cellular phone networks sheds light on how people move around on a large scale.
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paper_title: Centrality metric for dynamic networks

paper_content:

Centrality is an important notion in network analysis and is used to measure the degree to which network structure contributes to the importance of a node in a network. While many different centrality measures exist, most of them apply to static networks. Most networks, on the other hand, are dynamic in nature, evolving over time through the addition or deletion of nodes and edges. A popular approach to analyzing such networks represents them by a static network that aggregates all edges observed over some time period. This approach, however, under or overestimates centrality of some nodes. We address this problem by introducing a novel centrality metric for dynamic network analysis. This metric exploits an intuition that in order for one node in a dynamic network to influence another over some period of time, there must exist a path that connects the source and destination nodes through intermediaries at different times. We demonstrate on an example network that the proposed metric leads to a very different ranking than analysis of an equivalent static network. We use dynamic centrality to study a dynamic citations network and contrast results to those reached by static network analysis.
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paper_title: Mesoscopic structure and social aspects of human mobility

paper_content:

The individual movements of large numbers of people are important in many contexts, from urban planning to disease spreading. Datasets that capture human mobility are now available and many interesting features have been discovered, including the ultra-slow spatial growth of individual mobility. However, the detailed substructures and spatiotemporal flows of mobility--the sets and sequences of visited locations--have not been well studied. We show that individual mobility is dominated by small groups of frequently visited, dynamically close locations, forming primary "habitats" capturing typical daily activity, along with subsidiary habitats representing additional travel. These habitats do not correspond to typical contexts such as home or work. The temporal evolution of mobility within habitats, which constitutes most motion, is universal across habitats and exhibits scaling patterns both distinct from all previous observations and unpredicted by current models. The delay to enter subsidiary habitats is a primary factor in the spatiotemporal growth of human travel. Interestingly, habitats correlate with non-mobility dynamics such as communication activity, implying that habitats may influence processes such as information spreading and revealing new connections between human mobility and social networks.
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paper_title: Human Mobility from GSM Data - A Valid Alternative to GPS?

paper_content:

Characteristics of human mobility are a valuable source of information in many applications. In this paper we evaluate the usability of call detail records for the extraction of mobility quantities. We derive several quantities from the simultaneously collected GPS and GSM mobility data of the Nokia Mobile Data Challenge. Our analyses show that GSM activity data underestimates average daily travel distance and radius of gyration when derived straightforward from the data. In addition, they indicate that the correlation between mobile phone usage and movement quantities is biased when using GSM activity data. Finally, our analyses conrm that long-term GSM activity data is well suited to detect frequent stop locations.
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paper_title: The scaling laws of human travel

paper_content:

The website wheresgeorge.com invites its users to enter the serial numbers of their US dollar bills and track them across America and beyond. Why? “For fun and because it had not been done yet”, they say. But the dataset accumulated since December 1998 has provided the ideal raw material to test the mathematical laws underlying human travel, and that has important implications for the epidemiology of infectious diseases. Analysis of the trajectories of over half a million dollar bills shows that human dispersal is described by a ‘two-parameter continuous-time random walk’ model: our travel habits conform to a type of random proliferation known as ‘superdiffusion’. And with that much established, it should soon be possible to develop a new class of models to account for the spread of human disease. The dynamic spatial redistribution of individuals is a key driving force of various spatiotemporal phenomena on geographical scales. It can synchronize populations of interacting species, stabilize them, and diversify gene pools1,2,3. Human travel, for example, is responsible for the geographical spread of human infectious disease4,5,6,7,8,9. In the light of increasing international trade, intensified human mobility and the imminent threat of an influenza A epidemic10, the knowledge of dynamical and statistical properties of human travel is of fundamental importance. Despite its crucial role, a quantitative assessment of these properties on geographical scales remains elusive, and the assumption that humans disperse diffusively still prevails in models. Here we report on a solid and quantitative assessment of human travelling statistics by analysing the circulation of bank notes in the United States. Using a comprehensive data set of over a million individual displacements, we find that dispersal is anomalous in two ways. First, the distribution of travelling distances decays as a power law, indicating that trajectories of bank notes are reminiscent of scale-free random walks known as Levy flights. Second, the probability of remaining in a small, spatially confined region for a time T is dominated by algebraically long tails that attenuate the superdiffusive spread. We show that human travelling behaviour can be described mathematically on many spatiotemporal scales by a two-parameter continuous-time random walk model to a surprising accuracy, and conclude that human travel on geographical scales is an ambivalent and effectively superdiffusive process.
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paper_title: Are call detail records biased for sampling human mobility?

paper_content:

Call detail records (CDRs) have recently been used in studying different aspects of human mobility. While CDRs provide a means of sampling user locations at large population scales, they may not sample all locations proportionate to the visitation frequency of a user, owing to sparsity in time and space of voice-calls, thereby introducing a bias. Also, as the rate of sampling is inherently dependent on the calling frequencies of an individual, high voice-call activity users are often chosen for conducting a meaningful study. Such a selection process can, inadvertently, lead to a biased view as high frequency callers may not always be representative of an entire population. With the advent of 3G technology and wide adoption of smart-phones, cellular devices have become versatile end-hosts. As the data accessed on these devices does not always require human initiation, it affords us with an unprecedented opportunity to validate the utility of CDRs for studying human mobility. In this work, we investigate various metrics for human mobility studied in literature for over a million cellular users in the San Francisco bay-area, for over a month. Our findings reveal that although the voice-call process does well to sample significant locations, such as home and work, it may in some cases incur biases in capturing the overall spatio-temporal characteristics of individual human mobility. Additionally, we motivate an "artificially" imposed sampling process, vis-a-vis the voice-call process with the same average intensity. We observe that in many cases such an imposed sampling process yields better performance results based on the usual metrics like entropies and marginal distributions used often in literature.
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paper_title: Spatiotemporal Data from Mobile Phones for Personal Mobility Assessment

paper_content:

Abstract ::: Purpose — In this chapter, we will review several alternative methods of collecting data from mobile phones for human mobility analysis. We propose considering cellular network location data as a useful complementary source for human mobility research and provide case studies to illustrate the advantages and disadvantages of each method. ::: ::: Methodology/approach — We briefly describe cellular phone network architecture and the location data it can provide, and discuss two types of data collection: active and passive localization. Active localization is something like a personal travel diary. It provides a tool for recording positioning data on a survey sample over a long period of time. Passive localization, on the other hand, is based on phone network data that are automatically recorded for technical or billing purposes. It offers the advantage of access to very large user populations for mobility flow analysis of a broad area. ::: ::: Findings — We review several alternative methods of collecting data from mobile phone for human mobility analysis to show that cellular network data, although limited in terms of location precision and recording frequency, offer two major advantages for studying human mobility. First, very large user samples – covering broad geographical areas – can be followed over a long period of time. Second, this type of data allows researchers to choose a specific data collection methodology (active or passive), depending on the objectives of their study. The big mobile phone localization datasets have provided a new impulse for the interdisciplinary research in human mobility. ::: ::: Originality/value of chapter — We propose considering cellular network location data as a useful complementary source for transportation research and provide case studies to illustrate the advantages and disadvantages of each proposed method. Mobile phones have become a kind of “personal sensor” offering an ever-increasing amount of location data on mobile phone users over long time periods. These data can thus provide a framework for a comprehensive and longitudinal study of temporal dynamics, and can be used to capture ephemeral events and fluctuations in day-to-day mobility behavior offering powerful tools to transportation research, urban planning, or even real-time city monitoring.
---
paper_title: Human mobility characterization from cellular network data

paper_content:

Anonymous location data from cellular phone networks sheds light on how people move around on a large scale.
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paper_title: Parsimonious Mobility Classification using GSM and WiFi Traces. Hot-EmNets

paper_content:

Human mobility states, such as dwelling, walking or driving, are a valuable primary and meta data type for transportation studies, urban planning, health monitoring and epidemiology. Previous work focuses on fine-grained location-based mobility inference using global positioning system (GPS) data and external geo-indexes such as map information. GPS-based mobility characterization raises practical issues related to spotty coverage and battery drain, but the more fundamental concern addressed in this paper is that of privacy. For some applications and usage models we contend that it is desirable to adopt a more parsimonious approach to mobility characterization; one that avoids the collection and use of fine-grained location information by relying instead on GSM and WiFi connectivity data. Building upon previous work that demonstrated the utility of using GSM and WiFi beacons for localization applications, we demonstrate that this approach to mobility classification achieves promising results (with accuracy of 88%) using a sample data set collected across several populated areas in Los Angeles and is worthy of further research.
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paper_title: Network Centrality of Metro Systems

paper_content:

Whilst being hailed as the remedy to the world's ills, cities will need to adapt in the 21(st) century. In particular, the role of public transport is likely to increase significantly, and new methods and technics to better plan transit systems are in dire need. This paper examines one fundamental aspect of transit: network centrality. By applying the notion of betweenness centrality to 28 worldwide metro systems, the main goal of this paper is to study the emergence of global trends in the evolution of centrality with network size and examine several individual systems in more detail. Betweenness was notably found to consistently become more evenly distributed with size (i.e. no "winner takes all") unlike other complex network properties. Two distinct regimes were also observed that are representative of their structure. Moreover, the share of betweenness was found to decrease in a power law with size (with exponent 1 for the average node), but the share of most central nodes decreases much slower than least central nodes (0.87 vs. 2.48). Finally the betweenness of individual stations in several systems were examined, which can be useful to locate stations where passengers can be redistributed to relieve pressure from overcrowded stations. Overall, this study offers significant insights that can help planners in their task to design the systems of tomorrow, and similar undertakings can easily be imagined to other urban infrastructure systems (e.g., electricity grid, water/wastewater system, etc.) to develop more sustainable cities.
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paper_title: Augmented Betweenness Centrality for Mobility Prediction in Transportation Networks

paper_content:

Measuring and predicting the human mobility along the links of a transportation network has always been of a great importance to researchers in the field. Hitherto, producing such data relied heavily on expensive and time con- suming surveying and on-field observational methods. In this work we propose an efficient estimation method for the assessment of the flow through links in trans- portation networks that is based on the Betweenness Centrality measure of the network's nodes. Furthermore, we show that the correlation between those two features can be significantly increased when additional (pre-defined and known) properties of the network are taken into account, generating an augmented Mo- bility Oriented Betweenness Centrality measure. We validate the results using a transportation dataset, constructed using cellular phones data, that contains a high resolution network of the Israeli transportation system. We show that the flow that was measured using this expensive and complicated method can be accurately es- timated using our proposed Augmented Betweenness technique.
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paper_title: A PCA-based approach for exploring space-time structure of urban mobility dynamics

paper_content:

Understanding of urban mobility dynamics benefits both aggregate human mobility in wireless communications, and the planning and provision of urban facilities and services. Due to the high penetration of cell phones, the cellular networks provide information for urban dynamics with large spatial extent and continuous temporal coverage. In this paper, a novel approach is proposed to explore the space-time structure of urban dynamics, based on the original data collected by cellular networks in a southern city of China, recording population distribution by dividing the city into thousands of pixels. By applying principal component analysis, the intrinsic dimensionality is revealed. The structure of all the pixel population variations could be well captured by a small set of eigen pixel population variations. According to the classification of eigen pixel population variations, each pixel population variation can be decomposed into three constitutions: deterministic trends, short-lived spikes, and noise. Moreover, the most significant eigen pixel population variations are utilized in the applications of forecasting and anomaly detection.
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paper_title: Spatiotemporal Data from Mobile Phones for Personal Mobility Assessment

paper_content:

Abstract ::: Purpose — In this chapter, we will review several alternative methods of collecting data from mobile phones for human mobility analysis. We propose considering cellular network location data as a useful complementary source for human mobility research and provide case studies to illustrate the advantages and disadvantages of each method. ::: ::: Methodology/approach — We briefly describe cellular phone network architecture and the location data it can provide, and discuss two types of data collection: active and passive localization. Active localization is something like a personal travel diary. It provides a tool for recording positioning data on a survey sample over a long period of time. Passive localization, on the other hand, is based on phone network data that are automatically recorded for technical or billing purposes. It offers the advantage of access to very large user populations for mobility flow analysis of a broad area. ::: ::: Findings — We review several alternative methods of collecting data from mobile phone for human mobility analysis to show that cellular network data, although limited in terms of location precision and recording frequency, offer two major advantages for studying human mobility. First, very large user samples – covering broad geographical areas – can be followed over a long period of time. Second, this type of data allows researchers to choose a specific data collection methodology (active or passive), depending on the objectives of their study. The big mobile phone localization datasets have provided a new impulse for the interdisciplinary research in human mobility. ::: ::: Originality/value of chapter — We propose considering cellular network location data as a useful complementary source for transportation research and provide case studies to illustrate the advantages and disadvantages of each proposed method. Mobile phones have become a kind of “personal sensor” offering an ever-increasing amount of location data on mobile phone users over long time periods. These data can thus provide a framework for a comprehensive and longitudinal study of temporal dynamics, and can be used to capture ephemeral events and fluctuations in day-to-day mobility behavior offering powerful tools to transportation research, urban planning, or even real-time city monitoring.
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paper_title: CONGESTION AND CENTRALITY IN TRAFFIC FLOW ON COMPLEX NETWORKS

paper_content:

The central points of communication network flow have often been identified using graph theoretical centrality measures. In real networks, the state of traffic density arises from an interplay between the dynamics of the flow and the underlying network structure. In this work we investigate the relationship between centrality measures and the density of traffic for some simple particle hopping models on networks with emerging scale-free degree distributions. We also study how the speed of the dynamics are affected by the underlying network structure. Among other conclusions, we find that, even at low traffic densities, the dynamical measure of traffic density (the occupation ratio) has a non-trivial dependence on the static centrality (quantified by "betweenness centrality"), where non-central vertices get a comparatively large portion of the traffic.
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paper_title: Managing Socio-spatial Data as Large Graphs

paper_content:

A socio-spatial graph is a combination of a social network with a spatial network. In such graph, the social network contains information on users and about the social relationships among these users. The spatial network contains information on geographic entities and about the spatial relationships among these entities. Users are associated with geographic locations using life-pattern edges. The life pattern edges synopsize the location history of people, and accordingly, connect individuals to places they frequently visit. Such graphs are used to provide information on people, while taking into account the spatial whereabout of individuals, and to provide information on geographical entities, in correspondence with their social aspects, i.e., according to the people visited in these entities. Thus, socio-spatial graphs are important analytic tools, however, when they combine a large social network with a large spatial network, the result is a large graph. In this paper we show how to eciently build such large graphs and how to query them eectively.
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paper_title: SUMO – Simulation of Urban MObility An Overview

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SUMO is an open source traffic simulation package including net import and demand modeling components. We describe the current state of the package as well as future developments and extensions. SUMO helps to investigate several research topics e.g. route choice and traffic light algorithm or simulating vehicular communication. Therefore the framework is used in different projects to simulate automatic driving or traffic management strategies.
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paper_title: Diversity of Individual Mobility Patterns

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Uncovering human mobility patterns is of fundamental importance to the understanding of epidemic spreading, urban transportation and other socioeconomic dynamics embodying spatiality and human travel. The observed scaling laws for aggregated data require a theoretical explanation of their underlying mechanism. According to the direct travel diaries of volunteers, we show the absence of scaling properties in the displacement distribution at the individual level, which unfortunately provides a complete contrast to most inferences and assumptions in the literature. The aggregated displacement distribution follows a power law with an exponential cutoff, which is analytically explained by the mixture nature of human travel under Maxwell-Boltzmann statistics. Our analysis provides an alternative way to bridge diverse patterns at the individual level and scaling laws at the population level.
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paper_title: Growing multiplex networks

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We propose a modeling framework for growing multiplexes where a node can belong to different networks. We define new measures for multiplexes and we identify a number of relevant ingredients for modeling their evolution such as the coupling between the different layers and the distribution of node arrival times. The topology of the multiplex changes significantly in the different cases under consideration, with effects of the arrival time of nodes on the degree distribution, average shortest path length, and interdependence.
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paper_title: Understanding individual human mobility patterns

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Despite their importance for urban planning, traffic forecasting and the spread of biological and mobile viruses, our understanding of the basic laws governing human motion remains limited owing to the lack of tools to monitor the time-resolved location of individuals. Here we study the trajectory of 100,000 anonymized mobile phone users whose position is tracked for a six-month period. We find that, in contrast with the random trajectories predicted by the prevailing Lévy flight and random walk models, human trajectories show a high degree of temporal and spatial regularity, each individual being characterized by a time-independent characteristic travel distance and a significant probability to return to a few highly frequented locations. After correcting for differences in travel distances and the inherent anisotropy of each trajectory, the individual travel patterns collapse into a single spatial probability distribution, indicating that, despite the diversity of their travel history, humans follow simple reproducible patterns. This inherent similarity in travel patterns could impact all phenomena driven by human mobility, from epidemic prevention to emergency response, urban planning and agent-based modelling.
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paper_title: Spatiotemporal Data from Mobile Phones for Personal Mobility Assessment

paper_content:

Abstract ::: Purpose — In this chapter, we will review several alternative methods of collecting data from mobile phones for human mobility analysis. We propose considering cellular network location data as a useful complementary source for human mobility research and provide case studies to illustrate the advantages and disadvantages of each method. ::: ::: Methodology/approach — We briefly describe cellular phone network architecture and the location data it can provide, and discuss two types of data collection: active and passive localization. Active localization is something like a personal travel diary. It provides a tool for recording positioning data on a survey sample over a long period of time. Passive localization, on the other hand, is based on phone network data that are automatically recorded for technical or billing purposes. It offers the advantage of access to very large user populations for mobility flow analysis of a broad area. ::: ::: Findings — We review several alternative methods of collecting data from mobile phone for human mobility analysis to show that cellular network data, although limited in terms of location precision and recording frequency, offer two major advantages for studying human mobility. First, very large user samples – covering broad geographical areas – can be followed over a long period of time. Second, this type of data allows researchers to choose a specific data collection methodology (active or passive), depending on the objectives of their study. The big mobile phone localization datasets have provided a new impulse for the interdisciplinary research in human mobility. ::: ::: Originality/value of chapter — We propose considering cellular network location data as a useful complementary source for transportation research and provide case studies to illustrate the advantages and disadvantages of each proposed method. Mobile phones have become a kind of “personal sensor” offering an ever-increasing amount of location data on mobile phone users over long time periods. These data can thus provide a framework for a comprehensive and longitudinal study of temporal dynamics, and can be used to capture ephemeral events and fluctuations in day-to-day mobility behavior offering powerful tools to transportation research, urban planning, or even real-time city monitoring.
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paper_title: Unique in the Crowd: The privacy bounds of human mobility

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We study fifteen months of human mobility data for one and a half million individuals and find that human mobility traces are highly unique. In fact, in a dataset where the location of an individual is specified hourly, and with a spatial resolution equal to that given by the carrier's antennas, four spatio-temporal points are enough to uniquely identify 95% of the individuals. We coarsen the data spatially and temporally to find a formula for the uniqueness of human mobility traces given their resolution and the available outside information. This formula shows that the uniqueness of mobility traces decays approximately as the 1/10 power of their resolution. Hence, even coarse datasets provide little anonymity. These findings represent fundamental constraints to an individual's privacy and have important implications for the design of frameworks and institutions dedicated to protect the privacy of individuals.
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Title: A Survey on Human Mobility and Its Applications

Section 1: Introduction
Description 1: Discuss the significance of understanding human mobility for various sectors such as traffic systems, disease spread, and commercial applications.

Section 2: Related Works
Description 2: Provide an overview of existing studies on human mobility, including trajectory-based studies, statistical measures, and pattern extraction.

Section 3: Data Collection Techniques
Description 3: Describe different methods for collecting human mobility data, including surveys, GPS, WiFi scanning, and Call Data Records (CDR).

Section 4: Human Mobility Studies and Transportation
Description 4: Discuss the role of transportation networks in human mobility studies, and the impact of geographic and infrastructural factors.

Section 5: Traffic Flow in Human Mobility
Description 5: Explain the characteristics of traffic flows at microscopic and macroscopic levels, along with relevant variables and centrality measures.

Section 6: Tools and Applications
Description 6: Cover the computational tools and data storage solutions used for analyzing human mobility data, including databases and simulation frameworks.

Section 7: Discussion
Description 7: Highlight current challenges in human mobility studies, such as data pre-processing, map-matching, and integrating multiplex networks.

Section 8: Perspectives and Conclusion
Description 8: Summarize the findings and propose future directions in human mobility research, including new data types and behavioral aspects.