The influence of temporal and spatial features on the performance of next-place prediction algorithms

Baumann, Paul; Kleiminger, Wilhelm; Santini, Silvia

doi:10.1145/2493432.2493467

Cited by 55 publications

(28 citation statements)

References 18 publications

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“…However, compared to all previous studies this produced significantly higher upper bounds (although as their dataset is not publicly available it is not possible to replicate these results). We also note that some works such as [11]- [13] have considered the performance of pre-existing prediction algorithms. However, because we focus on the theoretical bounds of an optimal pre dictor rather than current algorithms their work is considered complementary rather than directly related.…”

Section: Related Workmentioning

confidence: 99%

A refined limit on the predictability of human mobility

Smith

Wieser

Goulding

et al. 2014

2014 IEEE International Conference on Pervasive Computing and Communications (PerCom)

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It has been recently claimed that human movement is highly predictable. While an upper bound of 93% predictability was shown, this was based upon human movement trajectories of very high spatiotemporal granularity. Recent studies reduced this spatiotemporal granularity down to the level of GPS data, and under a similar methodology results once again suggested a high predictability upper bound (i.e. 90% when movement was quantized down to a spatial resolution approximately the size of a large building). In this work we reconsider the derivation of the upper bound to movement predictability. By considering real-world topological constraints we are able to achieve a tighter upper bound, representing a more refined limit to the predictability of human movement. Our results show that this upper bound is between 11-24% less than previously claimed at a spatial resolution of approx. 100mx 100m, with a greater im provement for finer spatial resolutions. This indicates that human mobility is potentially less predictable than previously thought.We provide an in-depth examination of how varying the spatial and temporal quantization affects predictability, and consider the impact of corresponding limits using a large set of real-world GPS traces. Particularly at fine-grained spatial quantizations, where a significant number of practical applications lie, these new (lower) upper limits raise serious questions about the use of location information alone for prediction, contributing more evidence that such prediction must integrate external variables.

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Section: Related Workmentioning

confidence: 99%

A refined limit on the predictability of human mobility

Smith

Wieser

Goulding

et al. 2014

2014 IEEE International Conference on Pervasive Computing and Communications (PerCom)

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“…This way, we derive the set L of relevant places for each user. We use the same parameter settings as in [2] apart from the value of the sensitivity parameter which is set to 30% as in [9]. We then derive the mobility trace of each user using a s of 15 minutes.…”

Section: Evaluation Setupmentioning

confidence: 99%

“…The ability to predict when a person will arrive and how long she will stay at a specific place is fundamental to enable a number of applications like, e.g., smart heating control or urban navigation [5]. A number of algorithms that can perform these predictions have been presented in the literature [6,11,9]. This poster abstract presents our preliminary results on investigating both the theoretical and practical limits of the prediction performance achievable by arrival and residence time prediction algorithms.…”

Section: Introductionmentioning

confidence: 99%

How long are you staying?

Baumann

Kleiminger

Santini

2013

Proceedings of the 19th Annual International Conference on Mobile Computing &Amp; Networking - MobiCom '13

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“…Location prediction, in general, is using users' historical digital footprints to build their mobility models by statistical methods and predicting users' whereabouts by utilizing these models and related data. 14 The strategy and QoS of the various applications that mentioned above have all depended on the accuracy of user's location prediction. Therefore, predicting user's location accurately is of great significance to various fields.…”

Section: Introductionmentioning

confidence: 99%

A novel trajectory similarity–based approach for location prediction

Liu

et al. 2016

International Journal of Distributed Sensor Networks

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Location prediction impacts a wide range of research areas in mobile environment. The abundant mobility data, produced by mobile devices, make this research area attractive. Randomness makes people's future whereabouts hard to predict, although studies have proved that human mobility shows strong regularity. Most previous works, in general, tend to discover an association between a user's social relations in real world and variances in trajectory and then utilize this association to model the user's mobility which is used for location prediction. However, these methods normally require some specific data, which make them hard to be migrated to other platforms. Moreover, by focusing on social relations, these methods neglect the potential value of the associations among strangers' trajectory. Based on this argument, this article has proposed a novel location prediction approach trajectory similarity-based location prediction. It applies the social contagion theory and introduces a novel similarity computing-based trajectory method along with the trajectory sampling, which is achieved by covering algorithm to accelerate the process of computing similarity. Experiment results on real dataset show that trajectory similarity-based location prediction achieves higher accuracy and stability comparing to the state-of-the-art approaches.

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The influence of temporal and spatial features on the performance of next-place prediction algorithms

Cited by 55 publications

References 18 publications

A refined limit on the predictability of human mobility

A refined limit on the predictability of human mobility

How long are you staying?

A novel trajectory similarity–based approach for location prediction

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