Mobility profiler: A framework for discovering mobility profiles of cell phone users (original) (raw)
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Mobility Profiler : A Framework for Discovering Mobile User Profiles ( TECHNICAL REPORT Version )
2008
Mobility path information of cellphone users play a crucial role in a wide range of cellphone applications, including context-based search and advertising, early warning systems, city-wide sensing applications such as air pollution exposure estimation and traffic planning. However, there is a disconnect between the low level location data logs available from the cellphones and the high level mobility path information required to support these cellphone applications. In this paper, we present formal definitions to capture the cellphone users’ mobility patterns and profiles, and provide a complete framework, Mobility Profiler, for discovering mobile user profiles starting from cell based location log data. We use real-world cellphone log data (of over 350K hours of coverage) to demonstrate our framework and perform experiments for discovering frequent mobility patterns and profiles. Our analysis of mobility profiles of cellphone users expose a significant long tail in a user’s locatio...
On profiling mobility and predicting locations of wireless users
Proceedings of the 2nd international workshop on Multi-hop ad hoc networks: from theory to reality, 2006
In this paper, we analyze a year long wireless network users' mobility trace data collected on ETH Zurich campus. Unlike earlier work in [4, 19], we profile the movement pattern of wireless users and predict their locations. More specifically, we show that each network user regularly visits a list of places such as a building (also referred to as "hubs") with some probability. The daily list of hubs, along with their corresponding visit probabilities, are referred to as a mobility profile. We also show that over a period of time (e.g., a week), a user may repeatedly follow a mixture of mobility profiles with certain probabilities associated with each of the profiles. Our analysis of the mobility trace data not only validate the existence of our so-called sociological orbits [8], but also demonstrate the advantages of exploiting it in performing hub-level location predictions. In particular, we show that such profile based location predictions are more precise than common statistical approaches based on observed hub visitation frequencies alone.
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.
—Location prediction based on cellular network traces has recently spurred lots of interest. However, predicting one's location remains a very challenging task due to the randomness of the human mobility patterns. Our preliminary study included in this paper shows that there is a strong correlation and association among the certain group of users' locations. Through association pattern mining on Reality Mining dataset which involves 32,579 cell tower locations and 350,000 hours of continuous activity information, we observe the highly confident association rules exist among the locations of users, and then we further verify that the associations are indeed caused by the collective behaviors of the mobile phone users. Based on this finding we introduce the collective behavioral patterns (CBP), and then propose CBP-based predictor— a novel prediction schema that aims to forecasting one's locations in next 6 hours based on the locations of other users. Furthermore, we integrate the state-of-the-art i.e., Markov-based predictor with our CBP-based schema to build a hybrid predictor. We evaluate the CBP-based schema and compare the hybrid predictor with the Markov-based predictor through intensive experiments. Experimental results show that CBP-based predictor achieves good precision and the hybrid pre-dictor produces higher prediction accuracy than the state-of-the-art scheme at cell tower level in the forthcoming one to six hours. Finally it is verified that collective behavioral patterns can be used to predict user locations as well as to improve the performance of existing predictors.
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.
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.
Techniques and Applications to Analyze Mobility Data
Studies in Computational Intelligence, 2013
Mobility is intrinsic to human behavior and influences the dynamics of all social phenomena. As such, technology has not remained indifferent to the imprint of mobility. Today we are seeing a shift in tides as the focus is turning towards portability, as well as performance; mobile devices and wireless technologies have become ubiquitous in order to fulfil the needs of modern society. Today the need for mobility management is gradually becoming one of the most important and challenging problems in pervasive computing. In this chapter, we present an analysis of research activities targeting mobility. We present the challenges of analyzing and understanding the mobility (is mobility something that is inherently predictable? are humans socially inclined to follow certain paths?), to techniques that use mobility results to facilitate the interaction between peers in mobile networks, or detect the popularity of certain locations. Our studies are based on the analysis of real user traces extracted from volunteers. We emphasize the entire process of studying the dynamics of mobile users, from collecting the user data, to modelling mobility and interactions, and finally to exploring the predictability of human behavior. We point out the challenges and the limitations of such an endeavour. Furthermore, we propose techniques and methodologies to study the mobility and synergy of mobile users and we show their applicability on two case studies.
Clustering Users by Their Mobility Behavioral Patterns
ACM Transactions on Knowledge Discovery From Data, 2019
The immense stream of data from mobile devices during recent years enables one to learn more about human behavior and provide mobile phone users with personalized services. In this work, we identify clusters of users who share similar mobility behavioral patterns. We analyze trajectories of semantic locations to find users who have similar mobility "lifestyle," even when they live in different areas. For this task, we propose a new grouping scheme that is called Lifestyle-Based Clustering (LBC). We represent the mobility movement of each user by a Markov model and calculate the Jensen-Shannon distances among pairs of users. The pairwise distances are represented by a similarity matrix, which is used for the clustering. To validate the unsupervised clustering task, we develop an entropy-based clustering measure, namely, an index that measures the homogeneity of mobility patterns within clusters of users. The analysis is validated on a real-world dataset that contains location-movements of 50,000 cellular phone users that were analyzed over a two-month period.