Work2017

Next place prediction in unfamiliar places considering contextual factors

Takashi Maeda · Kota Tsubouchi · Fujio Toriumi

9 citations0/0 files storedACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (SIGSPATIAL GIS)

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

Abstract

This research aims to develop a method for maximizing the accuracy of next place prediction (NPP) in places that are unfamiliar to each mobile phone users. NPP is a problem of predicting the next place of the user given his/her current place and current time. In places that are unfamiliar to the person, it is difficult to predict the next place based on the person's historical location data because there are just a few or no data in such places for each user. Furthermore, it is also difficult to rely on the regularity of human mobility because tourists' mobility is easily affected by many external factors, such as weather. Our research aims to solve the difficulties in NPP in unfamiliar places by focusing on contextual factors such as weather, transportation means, place of residence, and time.

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References

2008Understanding individual human mobility patterns 2011Friendship and mobility 2003Using GPS to learn significant locations and predict movement across multiple users 2009Mapping the world's photos 2012Mining User Mobility Features for Next Place Prediction in Location-Based Services 2015CEPR 2015Topological Properties and Temporal Dynamics of Place Networks in Urban Environments

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2018Mobile Users Location Prediction with Complex Behavior Understanding 2021Tourist Transition Model among Tourist Attractions based on GPS Trajectory 2018Pedestrians Complex Behavior Understanding and Prediction with Hybrid Markov Chain

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