Robust Path Matching and Anomalous Route Detection Using Posterior Weighted Graphs

Abstract

Understanding movement behaviors is critical for urban mobility and transport problems, including robust path matching, behavior analysis, and anomaly detection. We investigate a graph-based, probabilistic method for matching behaviors of entities operating on networks embedded in some geographic context (e.g., road networks) under different types of uncertainty. Our method uses a decay function that allows network topology and attribute information associated with that topology (geographic or otherwise) to guide generalizations of the activity patterns and model learning process. This allows the system to recognize when two routes within a network are similar, even when those routes share little explicit path information. We demonstrate this method’s robust ability to distinguish between fundamentally different behaviors, even when data are both incomplete and subject to noise. The results show good performance when matching behaviors on different sized and attributed synthetic networks, as well as on a real-world road network; it examines situations in which observed entity behavior is noisy, as well as situations in which observed behaviors differ from learned models as a result of systemic noise in the underlying network. Finally, our approach provides a robust method of detecting anomalous activity patterns on the network.

References

1. Akinori Asahara, Kishiko Maruyama, Akiko Sato, and Kouichi Seto. 2011. Pedestrian-movement prediction based on mixed Markov-chain model. In Proceedings of the 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (GIS’11). ACM, New York, NY, 25--33.
2. Geoff Boeing. 2017. OSMnx: New methods for acquiring, constructing, analyzing, and visualizing complex street networks. Comput. Environ. Urban Syst. 65 (2017), 126--139.
3. Yingyi Bu, Lei Chen, Ada Wai-Chee Fu, and Dawei Liu. 2009. Efficient anomaly monitoring over moving object trajectory streams. In Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD’09). ACM, New York, NY, 159--168.
4. Varun Chandola, Arindam Banerjee, and Vipin Kumar. 2009. Anomaly detection: A survey. ACM Comput. Surv. 41, 3 (2009), 15.
5. Chao Chen, Daqing Zhang, Pablo Samuel Castro, Nan Li, Lin Sun, and Shijian Li. 2012. Real-time detection of anomalous taxi trajectories from GPS traces. In Mobile and Ubiquitous Systems: Computing, Networking, and Services. Springer, Berlin, 63--74.
6. Lei Chen, M. Tamer Özsu, and Vincent Oria. 2005. Robust and fast similarity search for moving object trajectories. In Proceedings of the 2005 ACM SIGMOD International Conference on Management of Data (SIGMOD’05). ACM, New York, NY, 491--502.
7. M. Duckham, L. Kulik, and M. Worboys. 2004. Imprecise navigation. GeoInformatica 7, 79 (2004).
8. C. Y. Goh, J. Dauwels, N. Mitrovic, M. T. Asif, A. Oran, and P. Jaillet. 2012. Online map-matching based on Hidden Markov model for real-time traffic sensing applications. In Proceedings from the 15th International IEEE Conference on Intelligent Transportation Systems. 776--781.
9. Bo Huang, Ruey Long Cheu, and Yong Seng Liew. 2004. GIS and genetic algorithms for HAZMAT route planning with security considerations. Int. J. Geogr. Inf. Sci. 18, 8 (2004), 769--787.
10. Hai Huang, Lijuan Zhang, and Monika Sester. 2014. A recursive Bayesian filter for anomalous behavior detection in trajectory data. In Connecting a Digital Europe through Location and Place. Springer, Berlin, 91--104.
11. Jung-Rae Hwang, Hye-Young Kang, and Ki-Joune Li. 2005. Spatio-temporal similarity analysis between trajectories on road networks. In Perspectives in Conceptual Modeling. Springer, Berlin, 280--289.
12. H. B. Johnson. 1976. Order upon the Land: The U.S. Rectangular Land Survey and the Upper Mississippi Country. Oxford University Press, Oxford.
13. B. Kuijpers and W. Othman. 2009. Modeling uncertainty of moving objects on road networks via space--time prisms. Int. J. Geogr. Inf. Sci. 23, 9 (2009), 1095--117.
14. Yinzhen Li, Ruichun He, Linzhong Liu, and Yaohuang Guo. 2005. Genetic algorithms for dissimilar shortest paths based on optimal fuzzy dissimilar measure and applications. In Proceedings of the 2nd International Conference on Fuzzy Systems and Knowledge Discovery, Volume I. 312--320.
15. Liang Liu, Clio Andris, and Carlo Ratti. 2010. Uncovering cabdrivers’ behavior patterns from their digital traces. Comput. Environ. Urban Syst. 34, 6 (2010), 541--548. <ce:title>GeoVisualization and the Digital City</ce:title> <ce:subtitle>Special issue of the International Cartographic Association Commission on GeoVisualization</ce:subtitle>.
16. Yin Lou, Chengyang Zhang, Yu Zheng, Xing Xie, Wei Wang, and Yan Huang. 2009. Map-matching for low-sampling-rate GPS trajectories. In Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems. 352--361.
17. Qifeng Lu, Feng Chen, and Kathleen Hancock. 2009. On path anomaly detection in a large transportation network. Comput. Environ. Urban Syst. 33, 6 (2009), 448--462.
18. Peter Mooney and Adam Winstanley. 2006. An evolutionary algorithm for multicriteria path optimization problems. Int. J. Geogr. Inf. Sci. 20, 4 (2006), 401--423.
19. Dieter Pfoser. 2002. Indexing the trajectories of moving objects. Data Eng. Bull. 25, 2 (2002), 4--10.
20. S. Porta, P. Crucitti, and V. Latora. 2006. The network analysis of urban streets: A dual approach. Physica A 369, 2 (2006).
21. S. Prager and R. P. Wiegand. 2014. Real-time detection of anomalous paths through networks. In Proceedings from the 17th AGILE Conference on Geographic Information Science.
22. Yukio Sadahiro, Raymond Lay, and Tetsuo Kobayashi. 2013. Trajectories of moving objects on a network: Detection of similarities, visualization of relations, and classification of trajectories. Trans. GIS 17, 1 (2013), 18--40.
23. Ramesh R. Sarukkai. 2000. Link prediction and path analysis using Markov chains. Comput. Network. 33, 1--6 (2000), 377--386.
24. W. M. Spears and S. D. Prager. 2012. Evolutionary search for understanding movement dynamics on mixed networks. GeoInformatica 17, 2 (2012), 353–385.
25. Eleftherios Tiakas, Apostolos N. Papadopoulos, Alexandros Nanopoulos, Yannis Manolopoulos, Dragan Stojanovic, and Slobodanka Djordjevic-Kajan. 2006. Trajectory similarity search in spatial networks. In Proceedings of the 10th International Database Engineering and Applications Symposium (IDEAS’06). 185--192.
26. Gonzalo Vaca-Castano, Amir Roshan Zamir, and Mubaral Shah. 2012. City scale geo-spatial trajectory estimation of a moving camera. In Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition.
27. R. Paul Wiegand and Steven Prager. 2013. Simple methods for reasoning about behavior patterns on graphs given extremely sparse observations. In Proceedings of the 5th International Conference on Advanced Geographic Information Systems, Applications, and Services (GEOProcessing’13). 73--80.
28. C. Zhang and W. Li. 2005. Markov chain modeling of multinomial land-cover classes. GISci. Remote Sens. 42, 1 (2005), 1--18.

Index Terms

• Information systems

  • Information systems applications

    • Spatial-temporal systems