Contextual conditional models for smartphone-based human mobility prediction

Tri, Trinh Minh; Gática-Pérez, Daniel

doi:10.1145/2370216.2370242

Cited by 90 publications

(57 citation statements)

References 27 publications

(22 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Gao et al [23] applied this approach to the Nokia Data Challenge dataset [24] using time and location features, and obtained an accuracy of approximately 50%. Do et al [25] applied the same technique but used a larger number of features including also SMS, calls and Bluetooth proximity, and obtained an accuracy of approximately 60%. In a subsequent paper [26] the same authors explore a kernel density estimation approach for improving performance.…”

Section: Related Workmentioning

confidence: 99%

Understanding predictability and exploration in human mobility

2018

View full text Add to dashboard Cite

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.

show abstract

Section: Related Workmentioning

confidence: 99%

Understanding predictability and exploration in human mobility

2018

View full text Add to dashboard Cite

show abstract

“…The challenges of such a problem reside in the limitations of data collection methods and inherent complexity of periodic behaviors [1] [5]. While in the research of large-scale human mobility system, the complexity in patterns of human mobility, migration and communication has been difficult to unpack, due to the availability of data and lack of sound theories.…”

Section: Introductionmentioning

confidence: 99%

Link prediction in human mobility networks

Yang

Chawla

Basu³

et al. 2013

Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining

View full text Add to dashboard Cite

Abstract-The understanding of how humans move is a longstanding challenge in the natural science. An important question is, to what degree is human behavior predictable? The ability to foresee the mobility of humans is crucial from predicting the spread of human to urban planning. Previous research has focused on predicting individual mobility behavior, such as the next location prediction problem. In this paper we study the human mobility behaviors from the perspective of network science. In the human mobility network, there will be a link between two humans if they are physically proximal to each other. We perform both microscopic and macroscopic explorations on the human mobility patterns. From the microscopic perspective, our objective is to answer whether two humans will be in proximity of each other or not. While from the macroscopic perspective, we are interested in whether we can infer the future topology of the human mobility network. In this paper we explore both problems by using link prediction technology, our methodology is demonstrated to have a greater degree of precision in predicting future mobility topology.

show abstract

“…Based on this representation, we were able to use statistical predictive methods such as random forest or least-square regression, and also an ensemble approach for combining multiple models. Compared to our recent work considering long range predictions of human mobility such as how long a user will stay in the current place [37], this paper focuses on very short future predictions. We also develop a more complete representation of context which allows us to learn useful generic models for predicting several dimensions of human mobile behavior.…”

Section: Related Workmentioning

confidence: 99%