Leveraging periodicity in human mobility for next place prediction

Bhaskar Prabhala · Budhaditya Deb · Thomas La Porta · Jiawei Han

10 citations0/0 files storedIEEE Wireless Communications and Networking Conference

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Human Mobility and Location-Based Analysis Data Management and Algorithms Traffic Prediction and Management Techniques

Abstract

Periodic transitions from place to place are inherent in human movements. Through visual examination we detect these periodic movements in traces of user tracking data. However such user tracking data sets tend to be sparse and incomplete. In addition, periodic movements are surrounded by noise: transitions to and from less frequently visited places and transitions to one of a kind visits. In this paper, we present algorithms leveraging techniques and models to detect periodicity in individual user movements. Our algorithms predict a user's next place given only the current context of timestamp and location. We apply these algorithms to real user mobility data sets. Prediction accuracy depends on the ratio of periodic movements to noise in user traces. For majority of users in a movement tracking data set collected over a year, our algorithms achieve next place prediction accuracies of 50% and above.

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References

2010Limits of Predictability in Human Mobility 2011Friendship and mobility 2009Inferring friendship network structure by using mobile phone data 2012Next place prediction using mobility Markov chains 2012The Mobile Data Challenge: Big Data for Mobile Computing Research 2011NextPlace: A Spatio-temporal Prediction Framework for Pervasive Systems 2009Mining correlation between locations using human location history

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2015Next place predictions based on user mobility traces 2020A Destination Prediction Model for Individual Passengers in Urban Rail Transit 2016A mobility prediction system leveraging realtime location data streams 2021A Survey of Open Source User Activity Traces with Applications to User Mobility Characterization and Modeling

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