2015 RegularityandConformityLocation

• (Wang et al., 2015) ⇒ Yingzi Wang, Nicholas Jing Yuan, Defu Lian, Linli Xu, Xing Xie, Enhong Chen, and Yong Rui. (2015). “Regularity and Conformity: Location Prediction Using Heterogeneous Mobility Data.” In: Proceedings of the 21st ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2015). ISBN:978-1-4503-3664-2 doi:10.1145/2783258.2783350

Subject Headings:

Cited By

• http://scholar.google.com/scholar?q=%222015%22+Regularity+and+Conformity%3A+Location+Prediction+Using+Heterogeneous+Mobility+Data
• http://dl.acm.org/citation.cfm?id=2783258.2783350&preflayout=flat#citedby

Quotes

Author Keywords

• Collaborative filtering; conformity; data mining; gravity model; location prediction; location profile; regularity; spatial databases and gis; spatial influence

Abstract

Mobility prediction enables appealing proactive experiences for location-aware services and offers essential intelligence to business and governments. Recent studies suggest that human mobility is highly regular and predictable. Additionally, social conformity theory indicates that people's movements are influenced by others. However, existing approaches for location prediction fail to organically combine both the regularity and conformity of human mobility in a unified model, and lack the capacity to incorporate heterogeneous mobility datasets to boost prediction performance. To address these challenges, in this paper we propose a hybrid predictive model integrating both the regularity and conformity of human mobility as well as their mutual reinforcement. In addition, we further elevate the predictive power of our model by learning location profiles from heterogeneous mobility datasets based on a gravity model. We evaluate the proposed model using several city-scale mobility datasets including location check-ins, GPS trajectories of taxis, and public transit data. The experimental results validate that our model significantly outperforms state-of-the-art approaches for mobility prediction in terms of multiple metrics such as accuracy and percentile rank. The results also suggest that the predictability of human mobility is time-varying, e.g., the overall predictability is higher on workdays than holidays while predicting users' unvisited locations is more challenging for workdays than holidays.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2015 RegularityandConformityLocation	Enhong Chen Xing Xie Defu Lian Yong Rui Yingzi Wang Nicholas Jing Yuan Linli Xu			Regularity and Conformity: Location Prediction Using Heterogeneous Mobility Data				10.1145/2783258.2783350		2015