1,721,015 research outputs found
MyWay: Location prediction via mobility profiling
No full textForecasting the future positions of mobile users is a valuable task allowing us to operate efficiently a myriad of different applications which need this type of information. We propose MyWay, a prediction system which exploits the individual systematic behaviors modeled by mobility profiles to predict human movements. MyWay provides three strategies: the individual strategy uses only the user individual mobility profile, the collective strategy takes advantage of all users individual systematic behaviors, and the hybrid strategy that is a combination of the previous two. A key point is that MyWay only requires the sharing of individual mobility profiles, a concise representation of the user's movements, instead of raw trajectory data revealing the detailed movement of the users. We evaluate the prediction performances of our proposal by a deep experimentation on large real-world data. The results highlight that the synergy between the individual and collective knowledge is the key for a better prediction and allow the system to outperform the state-of-art methods
Driving profiles computation and monitoring for car insurance CRM
No full textCustomer segmentation is one of the most traditional and valued tasks in customer relationship management (CRM). In this article, we explore the problem in the context of the car insurance industry, where the mobility behavior of customers plays a key role: Different mobility needs, driving habits, and skills imply also different requirements (level of coverage provided by the insurance) and risks (of accidents). In the present work, we describe a methodology to extract several indicators describing the driving profile of customers, and we provide a clustering-oriented instantiation of the segmentation problem based on such indicators. Then, we consider the availability of a continuous flow of fresh mobility data sent by the circulating vehicles, aiming at keeping our segments constantly up to date. We tackle a major scalability issue that emerges in this context when the number of customers is large-namely, the communication bottleneck-by proposing and implementing a sophisticated distributed monitoring solution that reduces communications between vehicles and company servers to the essential. We validate the framework on a large database of real mobility data coming from GPS devices on private cars. Finally, we analyze the privacy risks that the proposed approach might involve for the users, providing and evaluating a countermeasure based on data perturbation
Efficient distributed computation of human mobility aggregates through user mobility profiles
No full text
City users' classification with mobile phone data
No full textNowadays mobile phone data are an actual proxy for studying the users' social life and urban dynamics. In this paper we present the Sociometer, and analytical framework aimed at classifying mobile phone users into behavioral categories by means of their call habits. The analytical process starts from spatio-temporal profiles, learns the different behaviors, and returns annotated profiles. After the description of the methodology and its evaluation, we present an application of the Sociometer for studying city users of one small and one big city, evaluating the impact of big events in these cities
There's a Path for Everyone: A Data-Driven Personal Model Reproducing Mobility Agendas
No full textThe avalanche of mobility data like GPS and GSM daily produced by each user through mobile devices enables personalized mobility-services improving everyday life. The base for these mobility-services lies in the predictability of human behavior. In this paper we propose an approach for reproducing the user's personal mobility agenda that is able to predict the user's positions for the whole day. We reproduce the agenda by exploiting a data-driven personal mobility model able to capture and summarize different aspects of the systematic mobility behavior of a user. We show how the proposed approach outperforms typical methodologies adopted in the literature on four different real GPS datasets. Moreover, we analyze some features of the mobility models and we discuss how they can be employed as agents of a simulator for what-if mobility analysis
A Query Language for Mobility Data Mining.
No full textThe technologies of mobile communications and ubiquitous computing pervade society. Wireless networks sense the movement of people and vehicles, generating large volumes of mobility data, such as mobile phone call records and GPS tracks. This data can produce useful knowledge, supporting sustainable mobility and intelligent transportation systems, provided that a suitable knowledge discovery process is enacted for mining this mobility data. In this paper, the authors examine a formal framework, and the associated implementation, for a data mining query language for mobility data, created as a result of a European-wide research project called GeoPKDD (Geographic Privacy-Aware Knowledge Discovery and Delivery). The authors discuss how the system provides comprehensive support for the Mobility Knowledge Discovery process and illustrate its analytical power in unveiling the complexity of urban mobility in a large metropolitan area, based on a massive real life GPS dataset.</p
- …
