On the properties of human mobility (original) (raw)
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Discovering locations and habits from human mobility data
Annals of Telecommunications, 2020
Human mobility patterns are associated with many aspects of our life. With the increase of the popularity and pervasiveness of smartphones and portable devices, the Internet of Things (IoT) is turning into a permanent part of our daily routines. Positioning technologies that serve these devices such as the cellular antenna (GSM networks), global navigation satellite systems (GPS), and more recently the WiFi positioning system (WPS) provide large amounts of spatio-temporal data in a continuous way (data streams). In order to understand human behavior, the detection of important places and the movements between these places is a fundamental task. That said, the proposal of this work is a method for discovering user habits over mobility data without any a priori or external knowledge. Our approach extends a density-based clustering method for spatio-temporal data to identify meaningful places the individuals' visit. On top of that, a Gaussian mixture model (GMM) is employed over movements between the visits to automatically separate the trajectories accordingly to their key identifiers that may help describe a habit. By regrouping trajectories that look alike by day of the week, length, and starting hour, we discover the individual's habits. The evaluation of the proposed method is made over three real-world datasets. One dataset contains high-density GPS data and the others use GSM mobile phone data with 15-min sampling rate and Google Location History data with a variable sampling rate. The results show that the proposed pipeline is suitable for this task as other habits rather than just going from home to work and vice versa were found. This method can be used for understanding person behavior and creating their profiles revealing a panorama of human mobility patterns from raw mobility data.
Proceedings of the 15th International Conference on emerging Networking EXperiments and Technologies, 2019
The prediction of individuals' dynamics has attracted significant community attention and has implication for many fields: e.g. epidemic spreading, urban planning, recommendation systems. Current prediction models, however, are unable to capture uncertainties in the mobility behavior of individuals, and consequently, suffer from the inability to predict visits to new places. This is due to the fact that current models are oblivious to the exploration aspect of human behavior. This paper contributes better understanding of this aspect and presents a new strategy for identifying exploration profiles of a population. Our strategy captures spatiotemporal properties of visits-i.e. a known or new location (spatial) as well as a recurrent and intermittent visit (temporal)-and classifies individuals as scouters (i.e., extreme explorers), routineers (i.e., extreme returners), or regulars (i.e., with a medium behavior). To the best of our knowledge, this is the first work profiling spatiotemporal exploration of individuals in a simple and easy-to-implement way, with the potential to benefit services relying on mobility prediction.
Inferring Urban Mobility and Habits from User Location History
Transportation Research Procedia
Retrieving exhaustive information about individual mobility patterns is an essential step in order to implement effective mobility solutions. Despite their popularity, digital travel surveys still require a significant amount of inputs from the respondent. Consequently, they require great efforts from both respondents and analysts, and are limited to a relatively short period of timebetween a few weeks and a year. Driven by these motivations, the approach proposed in this paper uses mobile phone location history to automatically detect activity location without any interaction with the respondent. The proposed methodology uses raw location data together with a special indexing technique to calculate the probability of performing a certain activity in a certain location. It uses a heuristic rule to improve this estimation by considering the value of information over time. Finally, GIS data about the number of facilities located in a certain area is downloaded in real-time to further improve the overall estimation. Results of this exploratory study support the idea that the proposed approach can reconstruct complex mobility patterns while minimizing the number of active inputs from the respondent.
Where you stop is who you are: understanding people’s activities by places visited
The increasing availability of people traces -collected by portable devices -poses new possibilities and challenges for the study of people mobile behaviour. However, the raw data produced by such portable devices is poor from a semantic point of view, thus the gap between the person's activity and the raw collected data generated by the activity is still too wide. The work presented in this paper aims to define an algorithm to understand the activity of a moving person from the sequence of places she visited. The contribution is twofold. On one hand, an algorithm to associate each stop of the traveling person to a list of probable visited places is introduced. On the other hand, the obtained sequence of places is classified into a possible activity performed by the moving person. Preliminary experimental results on a dataset of people moving by car in the city of Milan are reported.
Inferring human mobility using communication patterns
Understanding the patterns of mobility of individuals is crucial for a number of reasons, from city planning to disaster management. There are two common ways of quantifying the amount of travel between locations: by direct observations that often involve privacy issues, e.g., tracking mobile phone locations, or by estimations from models. Typically, such models build on accurate knowledge of the population size at each location. However, when this information is not readily available, their applicability is rather limited. As mobile phones are ubiquitous, our aim is to investigate if mobility patterns can be inferred from aggregated mobile phone call data alone. Using data released by Orange for Ivory Coast, we show that human mobility is well predicted by a simple model based on the frequency of mobile phone calls between two locations and their geographical distance. We argue that the strength of the model comes from directly incorporating the social dimension of mobility. Furthermore, as only aggregated call data is required, the model helps to avoid potential privacy problems. P eople travel and move for a variety of reasons, including social, economic, and political factors. While individuals may follow simple, recurrent patterns of movement, e.g., daily commuting, a more complex picture emerges when all trajectories of a population are assembled together 1 . Understanding the principles governing individual and collective movement is important for a number of reasons: for planning urban design 2 , for forecasting and avoiding traffic congestion 3 , for mitigating infectious disease 4-6 , and for contingency planning in extreme situations caused by disasters 7,8 . However, accurately determining the movement patterns in a population is cumbersome and costly, and involves privacy issues.
Mining Human Mobility Data to Discover Locations and Habits
Machine Learning and Knowledge Discovery in Databases, 2020
Many aspects of life are associated with places of human mobility patterns and nowadays we are facing an increase in the pervasiveness of mobile devices these individuals carry. Positioning technologies that serve these devices such as the cellular antenna (GSM networks), global navigation satellite systems (GPS), and more recently the WiFi positioning system (WPS) provide large amounts of spatio-temporal data in a continuous way. Therefore, detecting significant places and the frequency of movements between them is fundamental to understand human behavior. In this paper, we propose a method for discovering user habits without any a priori or external knowledge by introducing a densitybased clustering for spatio-temporal data to identify meaningful places and by applying a Gaussian Mixture Model (GMM) over the set of meaningful places to identify the representations of individual habits. To evaluate the proposed method we use two real-world datasets. One dataset contains high-density GPS data and the other one contains GSM mobile phone data in a coarse representation. The results show that the proposed method is suitable for this task as many unique habits were identified. This can be used for understanding users' behavior and to draw their characterizing profiles having a panorama of the mobility patterns from the data.
From mobility data to habits and common pathways
Expert Systems, 2020
Many aspects of our lives are associated with places and the activities we perform on a daily basis. Most of them are recurrent and demand displacement of the individual between regular places like going to work, school or other important personal locations. To accomplish these recurrent daily activities, people tend to follow regular paths with similar temporal and spatial characteristics, especially because humans are frequently looking for uniformity to support their decisions and make their actions easier or even automatic. In this work, we propose a method for discovering common pathways across users' habits from human mobility data. By using a density-based clustering algorithm, we identify the most preferable locations the users visit, we apply a Gaussian mixture model over these places to automatically separate among all traces, the trajectories that follow patterns in order to discover the representations of individual's habits. By using the longest common sub-sequence algorithm, we search for the trajectories that are more similar over the set of users' habits trips by considering the distance that pairs of users or habits share on the same path. The proposed method is evaluated over two real-world GPS datasets and the results show that the approach is able to detect the most important places in a user's life, detect the routine activities and identify common routes between users that have similar habits paving the way for research techniques in carpooling, recommendation and prediction systems.
Minimizing User Involvement for Learning Human Mobility Patterns from Location Traces
Proceedings of the AAAI Conference on Artificial Intelligence
Utilizing trajectories for modeling human mobility often involves extracting descriptive features for each individual, a procedure heavily based on experts' knowledge. In this work, our objective is to minimize human involvement and exploit the power of community in learning `features' for individuals from their location traces. We propose a probabilistic graphical model that learns distribution of latent concepts, named motifs, from anonymized sequences of user locations. To handle variation in user activity level, our model learns motif distributions from sequence-level location co-occurrence of all users. To handle the big variation in location popularity, our model uses an asymmetric prior, conditioned on per-sequence features. We evaluate the new representation in a link prediction task and compare our results to those of baseline approaches.
Scalable and privacy-respectful interactive discovery of place semantics from human mobility traces
Information Visualization, 2015
Mobility diaries of a large number of people are needed for assessing transportation infrastructure and spatial development planning. Acquisition of personal mobility diaries through population surveys is a costly and error-prone endeavour. We examine an alternative approach to obtaining similar information from episodic digital traces of people’s presence in various locations, which appear when people use their mobile devices for making phone calls, accessing the Internet or posting georeferenced contents (texts, photos or videos) in social media. Having episodic traces of a person over a long time period, it is possible to detect significant (repeatedly visited) personal places and identify them as home, work or place of social activities based on temporal patterns of a person’s presence in these places. Such analysis, however, can lead to compromising personal privacy. We have investigated the feasibility of deriving place meanings and reconstructing personal mobility diaries whi...
On the regularity of human mobility
Understanding human mobility patterns is crucial to fields such as urban mobility and mobile network planning. For this purpose, we make use of large-scale datasets recording individuals spatio-temporal locations, from eight major world cities: Beijing, Tokyo, New York, Paris, San Francisco, London, Moscow and Mexico City. Our contributions are twofold: first, we show significant similarities in people's mobility habits regardless of the city and nature of the dataset. Second, we unveil three persistent traits present in an individual's urban mobility: repetitiveness, preference for shortest-paths, and confinement. These characteristics uncover people's tendency to revisit few favorite venues using the shortest-path available.