Context-aware next location prediction using data mining and metaheuristics
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Context‑aware next location prediction using data mining and metaheuristics Chetashri Bhadane1 · Ketan Shah1 Received: 11 February 2020 / Revised: 24 July 2020 / Accepted: 1 August 2020 © Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract Due to the heavy use of smartphones and other GPS enabled devices, researchers have easy access to substantial mobility data. Many existing techniques predict the next location of the users based on their mobility traces which includes only geographical coordinates in the form of spatio-temporal data. These raw mobility traces possess hidden information known as location context. Contextual information of any location means its name, time spent there, associated activity, preferred visit time and many such parameters. Enriching raw mobility traces with such contextual information adds more value to it and more sense to it’s applications. The proposed model performs geographical, contextual and behavioural enrichment of raw trajectories. It also assigns relevant tag to each identified location automatically using metaheuristic approach. This paper proposes a model CANLoc to perform data collection, trajectory enrichment and location prediction. Performance of the proposed model is verified using two datasets: GeoLife and Mobi-India, which shows significant improvement in the location prediction accuracy. Keywords Spatio-temporal data · Location prediction · Metaheuristics · Location context · HMM · Markov model
1 Introduction In recent years, massive mobility data is available for processing and knowledge extraction about human mobility. This data comes from various sources like GPS enabled mobile devices, tracking devices, geotagged photos as the movement of people leaves digital traces in the information systems. The wireless phone network provides mobile communication with an infrastructure to gather mobility data. Mobile communication invades our society and wireless networks detect the movement of people and generate a large amount of useful mobility data, such as mobile phone call records and Global Positioning System (GPS) tracks. People have also started sharing their mobility data and location preferences on various social networking sites like Facebook and Twitter. The knowledge discovery process on this data, addresses some fundamental questions of mobility
* Chetashri Bhadane [email protected] Ketan Shah [email protected] 1
SVKM’s MPSTME, Mumbai, India
analysts like where do the people travel frequently? How to characterize traffic jams and congestions? Mobility data mining is, therefore, emerging as a novel area of research and development. It aims to analyze mobility data by means of appropriate patterns and models extracted by efficient algorithms and techniques. It also aims at creating a knowledge discovery process explicitly designed for analysis of mobility with reference to geography and various other factors, at appropriate scales and granularity. Mobility Data Mining is usually composed of trajectories generation
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