現今由於路徑資料收集變得容易使的預測行動使用者移動目的地的預測越來越流行，大多數關於預測移動路徑的技術都需要使用者路徑資料地理上的模式吻合，所以這些技術在使用者未到達過的地區，可能無法使用。在這篇論文中，我們提出一個基於交通模式和行為語意特徵的方式來預測使用者移動的下一個位置。首先我們先辨識使用者的交通模式來得到一連串的使用者移動方式資料，接下來我們從使用者停留和活動的地方得到語意的意義作為使用者行為的語意特徵。我們透過隱馬可夫模型找出交通模式和行為語意特徵的關聯性來做出下一個位置的預測。我們使用真實的資料做為我們方法有用的證明。

Predicting movements of mobile users has become more and more popular because trajectory data collecting is easy nowadays. Most of those prediction techniques need geographic pattern matching of users’ trajectory data, so it is possible that those techniques cannot work in a place where the user has never been before. In this paper, we propose an approach based on transportation mode and behavior semantic features to predict the next location of the users’ movement. First, we identify the users’ transportation mode to get a sequential data of the users’ motion mode. Then, we get the semantic meaning as behavior semantic features from the places where users have stopped and visited for a while. We find the relationship between the transportation mode and behavior semantic features to predict the next location based on the Hidden Markov model. We use real world data for our experiment to demonstrate the effectiveness of our approach.

1. Microsoft’s GEOLIFE project:
http://reasearch.microsoft.com/en-us/projects/geolife/
2. Yu Zheng, Xing Xie and Wei-Ying Ma: GeoLife: A Collaborative Social Networking Service among User, Location and Trajectory, In 2010 IEEE.
3. Fumitaka Nakahara, Takahiro Murakami: A Destination Prediction Method Based on Behavioral Pattern Analysis Extracting from Nonperiodic Position Logs, In ACM LBSN 2011
4. Jia-Ching Ying, Wang-Chien Lee, Tz-Chiao Weng, Vincent S. Tseng: Semantic Trajectory Mining for Location Prediction, In ACM SIGSPATIAL GIS 2011
5. Rong Xie, Jun Luo, Yang Yue, Qingquan Li, Xiaoqing Zou: Pattern Mining, Semantic Label Identification and Movement Prediction Using Mobile Phone Data, In ADMA (International Conference on Advanced Data Mining and Applications) 2012
6. Hoyoung Jeung, Heng Tao Shen, and Xiaofang Zhou: Mining Trajectory Patterns Using Hidden Markov Models, In DaWaK 2007, LNCS 4654, pp. 470-480
7. Wesley Mathew, Ruben Raposo, Bruno Martins: Predicting Future Locations with Hidden Markov Models, In UbiComp 2012
8. Nam T. Nguyen, Dinh Q. Phung, Svetha Venkatesh, Hung Bui: Learning and Detecting Activities from Movement Trajectories Using the Hierarchical Hidden Markov Model
9. Yu Zheng, Quannan Li, Yukun Chen, Xing Xie, Wei-Ying Ma: Understanding Mobility Based on GPS Data, In UbiComp 2008
10. Leon Stenneth, Kenville Thompson, Waldin Stone, Jalal Alowibdi: Automated Transportation Transfer Detection Using GPS Enabled Smartphone, In International IEEE Conference on Intelligent Transportation Systems 2012
26
11. Ionut Trestian, Kevin Huguenin, Ling Su, Alekasndar Kuzmanovic: Transportation Transfer Detection Using GPS Enabled Smartphone, In International IEEE Conference on Intelligent Transportation Systems 2012
12. Zhixian Yan, Dipanjan Chakraborty, Christine Parent, Stefano Spaccapietra, Karl Aberer: SeMiTri: A Framework for Semantic Annotation of Heterogeneous Trajectories, In EDBT 2011
13. Zhixian Yan: Toward Semantic Trajectory Data Analysis: A Conceptual and Computational Approach
14. Mingqi Lv, Ling Chen, Gencai Chen: Discovering Personally Semantic Places from GPS Trajectories, In CIKM 2012
15. Xihui Chen, Jun Pang Ran Xue: Construsting and Comparing User Mobility Profiles for Location-based Services, In SAC 2013
16. Anna Monreale, Fabio Pinelli, Roberto Trasarti, Fosca Giannotti: WhereNext: a Location Predictor on Trajectory Pattern Mining, In KDD 2009
17. Yang Ye, Yu Zheng, Yukun Chen, Jianhua Feng, Xing Xie: Mining Individual Life Pattern Based on Location History: In 2009 Tenth International Conference on Mobile Data Management: Systems, Services and Middleware
18. Phil Blunsom: Hidden Markov Models, In 2004
19. L. R. .Rabiner, B. H. Juang: An Introduction to Hidden Markov Models, In IEEE ASSP MAGAZINE 1986
20. Zhiyuan Chen, Bahran Sanjabi, Dino Isa: A Location-Based User Movement Prediction Approach for Geolife Project, In International Journal of Computational Engineering Research (ijceronline.com) Vol. 2 Issue.7
21. Yu Zheng, Lizhu Zhang, Zhengxin Ma, Xing Xie, Wei-Ying Ma: Recommending Friends and Locations Based on Individual Location History, In ACM Transactions on the Web, 2011
22. Google: Google Places API: http://developers.google.com/places/
23. J. J. -C. Ying, E. H. –C. Lu, W.-C. Weng, V.S. Tseng: Mining User Similarity from Semantic Trajectories. In Proceedings of ACM SIGSPATIAL International Workshop on location Based Social Networks (LSBN’ 10), San Jose, California, USA, November 2, 2010
27
24. J48 Classifier http://weka.sourceforge .net/doc/weak/classifiers/trees/J48.html
25. Ling Chen, Mingqi Lv, Gencai Chen: Asystem for destination and future route prediction based on trajectory mining, In Journal Pervasive and Mobile Computing 6 (2010) 657–676
26. David D. Lewis: Naive (Bayes) at forty: The independence assumption in information retrieval, In Machine Learning: ECML-98 Lecture Notes in Computer Science Volume 1398, 1998, pp 4-15
27. J. A. K. Suykens, J. Vandewalle: Least Squares Support Vector Machine Classifiers, In Neural Processing Letters June 1999, Volume 9, Issue 3, pp293-300