Abstract
Predicting user behavior makes it possible to provide personalized services. Destination prediction (e.g. predicting a future location) can be applied to various practical applications. An example of destination prediction is personalized GIS services, which are expected to provide alternate routes to enable users to avoid congested roads. However, the destination prediction problem requires critical trade-offs between timing and accuracy. In this paper, we focus on early destination prediction as the central issue, as early recognition in destination prediction has not been fully explored. As an alternative to the traditional two basic approaches with trajectory tracking that narrow down the candidates with respect to the trip progress, and Next Place Prediction (NPP) that infers the future location of a user from user habits, we propose a new probabilistic model based on both conventional models. The advantage of our model is that it drastically narrows down the destination candidates efficiently at the early stage of a trip, owing to the staying information derived from the NPP approach. In other words, our approach achieves high prediction accuracy by considering both approaches at the same time. To implement our model, we employ SubSynE for state-of-the-art prediction based on trajectory tracking as well as a multi-class logistic regression based on user contexts. Despite the simplicity of our model, the proposed method provides improved performance compared to conventional approaches based on the experimental results using the GPS logs of 1,646 actual users from the commercial services.
- K. Alsabti, S. Ranka, and V. Singh. An efficient k-means clustering algorithm. In Proc. of High Prerformance Data Mining 1998 Workshop IPPS/SPDP.Google Scholar
- N. Beckmann, H.-P. Kriegel, R. Schneider, and B. Seeger. The R*-tree: An efficient and robust access method for points and rectangles. In Proc. of SIGMOD International Conference on Management of Data 1990, pages 332--331. Google ScholarDigital Library
- J. Duchi and Y. Singer. Efficient online and batch learning using forward backward splitting. J. of Machine Learning Research, pages 2899--2934, 2009.Google Scholar
- B. D.Ziebart, A. L.Maas, J. Bagnell, and A. K. Dey. Maximum entropy inverse reinforcement learning. In Proc. of AAAI 2008, pages 1433--1438.Google Scholar
- B. D.Ziebart, A. L.Maas, A. K. Dey, and J. Bagnell. Navigate like a cabbie: Probabilistic reasoning from observed context-aware behavior. In Proc. of Ubicomp 2008, pages 322--331.Google ScholarDigital Library
- M. Ester, H.-P. Kriegel, J. Sander, and X. Xu. Density-based algorithm for discovering clusters in large spatial databases with noise. In Proc. of KDD 1996, pages 226--231.Google Scholar
- H. Gao, J. Tang, and H. Liu. Mobile location prediction in spatio-temporal context. In Proc. of Pervasive Computing 2012 Workshop Nokia Mobile Data Challenge.Google Scholar
- S.-S. Ho and S. Ruan. Differential privacy for location pattern mining. In Proc of SPRINGL 2011, pages 17--24. Google ScholarDigital Library
- W. Huang, M. Li, and W. Hu. Hierarchical destination prediction based on GPS history. In Proc. of FSKD 2013, pages 972--977.Google Scholar
- T. Konishi, M. Maruyama, K. Tsubouchi, and M. Shimosaka. Cityprophet: city-scale irregularity prediction using transit app logs. In Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing, pages 752--757. ACM, 2016. Google ScholarDigital Library
- J. Krumm and E. Horvitz. Predestination: Infferring destinations from partial trajectories. In Proc. of UbiComp 2006, pages 243--260. Google ScholarDigital Library
- S. Levine, Z. Popovic, and V. Koltun. Feature construction for inverse reinforcement learning. In Advances in Neural Information Processing Systems, pages 1342--1350, 2010.Google Scholar
- C. Manasseh and R. Sengupta. Predicting driver destination using machine learning techniques. In Proc. of ITSC 2013, pages 142--147. Google ScholarCross Ref
- J. McInerney, J. Zheng, A. Rogers, and N. R. Jennings. Modelling heterogeneous location habits in human populations for location prediction under data sparsity. In Proc. of UbiComp 2013, pages 469--478. Google ScholarDigital Library
- S. McQuiggan, S. Lee, and J. Lester. Early prediction of student frustration. J. of ACII 2007, pages 698--709.Google Scholar
- A. Nadembega, T. Taleb, and A. Hafid. A destination prediction model based on historical data, contextual knowledge and spatial conceptual maps. In Proc. of ICC 2012, pages 1416--1420. Google ScholarCross Ref
- T. Okoshi, J. Ramos, H. Nozaki, J. Nakazawa, A. K. Dey, and H. Tokuda. Reducing users' perceived mental effort due to interruptive notifications in multi-device mobile environments. In Proc. of UbiComp 2015, pages 475--486. Google ScholarDigital Library
- G. Pan, G. Qi, and Z. Wu. Land-use classification using taxi GPS traces. In Trans. on Intelligent Transportation Systems, pages 113--123, 2013. Google ScholarDigital Library
- A. Parate, M. Bohmer, D. Chu, D. Ganesan, and B. M. Marlin. Practical prediction and prefetch for faster access to applications on mobile phones. In Proc. of UbiComp 2013, pages 275--284. Google ScholarDigital Library
- M. S. Ryoo. Human activity prediction: Early recognition of ongoing activities from streaming videos. In Proc of ICCV 2011, pages 1036--1043. Google ScholarDigital Library
- A. Sen and M. Larson. From sensors to songs: A learning-free novel music recommendation system using contextual sensor data. In Proc. of RecSys 2015 Workshop LocalRec.Google Scholar
- B. U. Töreyin, Y. Dedeoğlu, U. Güdükbay, and A. E. Cetin. Computer vision based method for real-time fire and flame detection. Pattern recognition letters, 27(1):49--58, 2006. Google ScholarDigital Library
- W. Woerndl, C. Schueller, and R. Wojtech. A hybrid recommender system for context-aware recommendation of mobile applications. In Proc. of ICDE 2007, pages 871--878. Google ScholarDigital Library
- A. Y. Xue, J. Qi, X. Xie, R. Zhang, J. Huang, and Y. Li. Solving the data sparsity problem in destination prediction. J. of VLDB 2015, pages 219--243.Google Scholar
- A. Y. Xue, R. Zhang, Y. Zheng, X. Xie, J. Huang, and Z. Xu. Destination prediction by sub-trajectory synthesis and privacy protection against such prediction. Proc of ICDE 2013, pages 254--265. Google ScholarDigital Library
- Y.-T. Zheng, Y. Li, Z.-J. Zha, and T.-S. Chua. Mining travel patterns from GPS-tagged photos. In Proc. of MMM 2011, pages 262--272. Google ScholarCross Ref
- Y. Zhu, Y. Sun, and Y. Wang. Nokia mobile data challenge: Predicting semantic place and next place via mobile data. In Proc. of Pervasive Computing 2012 Workshop Nokia Mobile Data Challenge.Google Scholar
Index Terms
- Early Destination Prediction with Spatio-temporal User Behavior Patterns
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