Modeling User Activity Patterns for Next-Place Prediction

Chen, Yu; Liu, Yang; Yao, Dezhong; Yang, Laurence T.; Jin, Hai; Chen, Hanhua; Ding, Qiang

doi:10.1109/jsyst.2015.2445919

Cited by 41 publications

(41 citation statements)

References 20 publications

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“…Providing a full solution for next explored place prediction is beyond the scope of this work, and here we simply aim to stress the fact that the prediction of explorations is very different from the predictions to returns to known places. Some previous work on next-place prediction using social information [29,30] or nearby Points Of Interest [43] may be the starting point for investigating this problem.…”

Section: Discussionmentioning

confidence: 99%

Understanding predictability and exploration in human mobility

2018

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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.

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Section: Discussionmentioning

confidence: 99%

Understanding predictability and exploration in human mobility

2018

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“…This means that further improvements of prediction accuracy should be based on the gathering and analysis of information from other sources. These sources can be: analysis of calls between users [14], social analysis of daily schedules that people share [7], [16], determination of social point of interests [17], social network analysis [18], combining cell tower location info with 802.11 access point location and GPS [19], user activity inferring and modelling [20].…”

Section: Discussionmentioning

confidence: 99%

Recent advances in location prediction methods for cellular communication networks

Leca

Tuta

Nicolaescu

et al. 2015

2015 23rd Telecommunications Forum Telfor (TELFOR)

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Predicting the future location of a device inside a cellular network has become an interesting field of study in recent years, as it offers new solutions for location based services. In this paper we analyze recently proposed methods for location prediction. Based on the proven limits of human predictability we propose that location prediction methods should not be limited only to movement pattern analysis. We conclude that location prediction methods can become more accurate by introducing social analysis methods based on call related information obtained from the network or the user's device.

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“…The second application is path recommendation from historical trajectories (Luo et al, 2013;Dai et al, 2015;Yang et al, 2017;Zheng et al, 2018). The third application is location prediction for tourists (Lee et al, 2016;Yu et al, 2017a), navigators (Li et al, 2016;Besse et al, 2018), and driverless vehicles . These applications require that the dynamic relationships of moving objects in space over time are inferred.…”

Section: Related Workmentioning

confidence: 99%

Relative space-based GIS data model to analyze the group dynamics of moving objects

Feng

Shaw

Fang

et al. 2019

ISPRS Journal of Photogrammetry and Remote Sensing

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The relative motion of moving objects is an essential research topic in geographical information science (GIScience), which supports the innovation of geodatabases, spatial indexing, and geospatial services. This analysis is very popular in the domains of urban governance, transportation engineering, logistics and geospatial information services for individuals or industrials. Importantly, data models of moving objects are one of the most crucial approaches to support the analysis for dynamic relative motion between moving objects, even in the age of big data and cloud computing. Traditional geographic information systems (GIS) usually organize moving objects as point objects in absolute coordinated space. The derivation of relative motions among moving objects is not efficient because of the additional geo-computation of transformation between absolute space and relative space. Therefore, current GISs require an innovative approach to directly store, analyze and interpret the relative relationships of moving objects to support their efficient analysis. This paper proposes a relative space-based GIS data model of moving objects (RSMO) to construct, operate and analyze moving objects' relationships and introduces two algorithms (relationship querying and relative relationship dynamic pattern matching) to derive and analyze the dynamic relationships of moving objects. Three scenarios (epidemic spreading, tracker finding, and motion-trend derivation of nearby crowds) are implemented to demonstrate the feasibility of the proposed model. The experimental results indicates the execution times of the proposed model are approximately 5-50% those of the absolute GIS method for the same function of these three scenarios. It's better computational performance of the proposed model when analyzing the relative relationships of moving objects than the absolute methods in a famous commercial GIS software based on this experimental results. The proposed approach fills the gap of traditional GIS and shows promise for relative space-based geo-computation, analysis and service.

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Modeling User Activity Patterns for Next-Place Prediction

Cited by 41 publications

References 20 publications

Understanding predictability and exploration in human mobility

Understanding predictability and exploration in human mobility

Recent advances in location prediction methods for cellular communication networks

Relative space-based GIS data model to analyze the group dynamics of moving objects

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