Contextual Conditional Models for Smartphone-based Human Mobility Prediction

Human behavior is often complex and context-dependent. This paper presents a general technique to exploit this ``multidimensional'' contextual variable for human mobility prediction. We use an ensemble method, in which we extract different mobility patterns with multiple models and then combine these models under a probabilistic framework. The key idea lies in the assumption that human mobility can be explained by several mobility patterns that depend on a subset of the contextual variables and these can be learned by a simple model. We showed how this idea can be applied to two specific online prediction tasks: \textit{what is the next place a user will visit?} and \textit{how long will he stay in the current place?}. Using smartphone data collected from 153 users during 17 months, we show the potential of our method in predicting human mobility in real life

Human Mobility Prediction Through Twitter.

Abstract Social media, in recent years, have become an invaluable source of information concerning human dynamics within urban context, allowing to enhance the comprehension of people behaviour, including human mobility regularities. The paper presents an approach to predict human mobility by exploiting Twitter data. The prediction approach is based on a novel trajectory pattern similarity measure that allows to identify the more suitable historic patterns to exploit for the prediction of the user next location. The pattern with the highest similarity to the user current trajectory will be used to predict the user next position. The experimental results obtained by using a real-world dataset show that the proposed method is effective in predicting the users next places achieving a remarkable precision

Creating Full Individual-level Location Timelines from Sparse Social Media Data

In many domain applications, a continuous timeline of human locations is critical; for example for understanding possible locations where a disease may spread, or the flow of traffic. While data sources such as GPS trackers or Call Data Records are temporally-rich, they are expensive, often not publicly available or garnered only in select locations, restricting their wide use. Conversely, geo-located social media data are publicly and freely available, but present challenges especially for full timeline inference due to their sparse nature. We propose a stochastic framework, Intermediate Location Computing (ILC) which uses prior knowledge about human mobility patterns to predict every missing location from an individual's social media timeline. We compare ILC with a state-of-the-art RNN baseline as well as methods that are optimized for next-location prediction only. For three major cities, ILC predicts the top 1 location for all missing locations in a timeline, at 1 and 2-hour resolution, with up to 77.2% accuracy (up to 6% better accuracy than all compared methods). Specifically, ILC also outperforms the RNN in settings of low data; both cases of very small number of users (under 50), as well as settings with more users, but with sparser timelines. In general, the RNN model needs a higher number of users to achieve the same performance as ILC. Overall, this work illustrates the tradeoff between prior knowledge of heuristics and more data, for an important societal problem of filling in entire timelines using freely available, but sparse social media data.Comment: 10 pages, 8 figures, 2 table

Mfingerprint: Privacy-preserving user modeling with multimodal mobile device footprints

Abstract. Mobile devices collect a variety of information about their environments, recording "digital footprints" about the locations and activities of their human owners. These footprints come from physical sensors such as GPS, WiFi, and Bluetooth, as well as social behavior logs like phone calls, application usage, etc. Existing studies analyze mobile device footprints to infer daily activities like driving/running/walking, etc. and social contexts such as personality traits and emotional states. In this paper, we propose a different approach that uses multimodal mobile sensor and log data to build a novel user modeling framework called mFingerprint that can effectively and uniquely depict users. mFingerprint does not expose raw sensitive information from the mobile device, e.g., the exact location, WiFi access points, or apps installed, but computes privacy-preserving statistical features to model the user. These descriptive features obscure sensitive information, and thus can be shared, transmitted, and reused with fewer privacy concerns. By testing on 22 users' mobile phone data collected over 2 months, we demonstrate the effectiveness of mFingerprint in user modeling and identification, with our proposed statistics achieving 81% accuracy across 22 users over 10-day intervals

Mfingerprint: Privacy-preserving user modeling with multimodal mobile device footprints

Abstract. The dramatic increase of daily usage of mobile devices generates massive digital footprints of users. Such footprints come from physical sensing such as GPS, WiFi, and Bluetooth, as well as social behavior sensing, e.g., call logs, application usage, etc. Many existing studies apply the mobile device footprints to infer daily activities like sitting/standing and social contexts such as personality traits and emotional states. In this paper, we propose a different approach to explore multimodal mobile footprints and build a novel user modeling framework called mFingerprint that can effectively and uniquely depict users. mFingerprint does not expose raw sensitive information from mobile device, e.g., the exact location, WiFi access points, or apps installed, but computes privacy-preserving statistical features to model the user discriminatively. These descriptive features protect sensitive information, thus can be shared, transmitted, and reused with less privacy concerns. By testing on 22 users' mobile phone data collected over 2 months, we demonstrate the effectiveness of mFingerprint in user modeling and identification. In particular, our conditional entropy footprint statistics can achieve 81% accuracy across all 22 users while evaluating over 10-day intervals

Understanding predictability and exploration in human mobility

Predictive models for human mobility have important applications in many fields including traffic control, ubiquitous computing, and contextual advertisement. The predictive performance of models in literature varies quite broadly, from over 90% to under 40%. In this work we study which underlying factors - in terms of modeling approaches and spatio-temporal characteristics of the data sources - have resulted in this remarkably broad span of performance reported in the literature. Specifically we investigate which factors influence the accuracy of next-place prediction, using a high-precision location dataset of more than 400 users observed for periods between 3 months and one year. We show that it is much easier to achieve high accuracy when predicting the time-bin location than when predicting the next place. Moreover, we demonstrate how the temporal and spatial resolution of the data have strong influence on the accuracy of prediction. Finally we reveal that the exploration of new locations is an important factor in human mobility, and we measure that on average 20-25% of transitions are to new places, and approx. 70% of locations are visited only once. We discuss how these mechanisms are important factors limiting our ability to predict human mobility

Classifying Spending Behavior using Socio-Mobile Data

Human spending behavior is essentially social. This work motivates and grounds the use of mobile phone based social interaction features for classifying spending behavior. Using a data set involving 52 adults (26 couples) living in a community for over a year, we find that social behavior measured via face-to-face interaction, call, and SMS logs, can be used to predict the spending behavior for couples in terms of their propensity to explore diverse businesses, become loyal customers, and overspend. Our results show that mobile phone based social interaction patterns can provide more predictive power on spending behavior than personality based features. Interestingly, we find that more social couples also tend to overspend. Obtaining such insights about couple level spending behavior via novel social-computing frameworks can be of vital importance to economists, marketing professionals, and policy makers.European Commission (PERSI project inside the Marie Curie Cofund 7th framework

The role of the social network and the usage of communication in travel behavior measured with Smartphone data

In this paper we investigate the use of a smartphone database to explore influences on travel behavior. Our aim is to exploit the rich individual-level data available from the smartphone to study the influence of communication and social contacts (collected via phone call and sms logs) on spatial movement (collected via GPS). An advantage of smartphone data is the ability to collect such rich data without user input over a long period of time, and the disadvantage is the difficulty associated with processing the data. We work with three months of data from 111 people collected via a snowball sample. In studying travel behavior, we focus on high level measures of mobility as represented by the size of one's activity space and one's travel intensity (our dependent variables). We use as explanatory variables sociodemographics, spatial relationship between home and work, communication use (number of phone calls and sms), and the travel behavior of those in the sample who are connected to the respondent (where connectivity is measured by phone and sms contact). We describe how these variables were processed from the smartphone data and present estimation results from the regression analysis. We find that people tend to travel in a similar manner as those they are socially connected to (consistent with the social network and travel literature) and that communication use is a compliment to physical travel (consistent with the telecommunication and travel literature). The results, although preliminary, illustrate how smartphone data can be exploited to reveal complex features of travel behavior