Daqing Zhang
ABSTRACT
GPS-equipped taxis can be viewed as pervasive sensors and the large-scale digital traces produced allow us to reveal many hidden "facts" about the city dynamics and human behaviors. In this paper, we aim to discover anomalous driving patterns from taxi's GPS traces, targeting applications like automatically detecting taxi driving frauds or road network change in modern cites. To achieve the objective, firstly we group all the taxi trajectories crossing the same source destination cell-pair and represent each taxi trajectory as a sequence of symbols. Secondly, we propose an Isolation-Based Anomalous Trajectory (iBAT) detection method and verify with large scale taxi data that iBAT achieves remarkable performance (AUC>0.99, over 90% detection rate at false alarm rate of less than 2%). Finally, we demonstrate the potential of iBAT in enabling innovative applications by using it for taxi driving fraud detection and road network change detection.
Supplemental Material
- N. Abe, B. Zadrozny, and J. Langford. Outlier detection by active learning. In Proc. KDD 2006, pages 504--509, 2006. Google Scholar
Digital Library
- D. Aha. Lazy Learning. Springer, 1997. Google ScholarDigital Library
- F. Angiulli and F. Fassetti. Dolphin: An efficient algorithm for mining distance-based outliers in very large datasets. ACM-TKDD, 3(1), 2009. Google ScholarDigital Library
- A. P. Bradley. The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pattern Recognition. 30(7):1145--1159, 1997. Google ScholarDigital Library
- M. M. Breunig, H.-P. Kriegel, R. T. Ng, and J. Sander. LOF: Identifying density-based local outliers. In Proc. SIGMOD 2000. pages 93--104, 2000. Google ScholarDigital Library
- Y. Bu, L. Chen, A. W.-C. Fu, and D. Liu. Efficient anomaly monitoring over moving object trajectory streams. In Proc. KDD 2009, pages 159--168, 2009. Google ScholarDigital Library
- L. Cao and J. Krumm. From GPS traces to a routable road map. In Proc. GIS 2009, pages 3--12, 2009. Google ScholarDigital Library
- J. Froehlich and J. Krumm. Route prediction from trip observations. In Proc. SAE 2008, pages 1031--1038, 2008.Google ScholarCross Ref
- Y. Ge, H. Xiong, Z.-H. Zhou, H. Ozdemir, J. Yu, and K. C. Lee. Top-Eye: Top-k evolving trajectory outlier detection. In Proc. CIKM 2010, pages 1733--1736, 2010. Google ScholarDigital Library
- M. C. Gonzalez, C. A. Hidalgo, and A.-L. Barabasi. Understanding individual human mobility patterns. Nature, 453(5):779--782, 2008.Google ScholarCross Ref
- Z. He, X. Xu, and S. Deng. Discovering cluster-based local outliers. Pattern Recognition Letters, 24(9-10):1641--1650, 2003. Google ScholarDigital Library
- E. M. Knorr, R. T. Ng, and V. Tucakov. Distance-based outliers: Algorithms and applications. VLDB Journal, 8(3-4):237--253, 2000. Google ScholarDigital Library
- J. Krumm and E. Horvitz. Predestination: Inferring destinations from partial trajectories. In Proc. Ubicomp 2006, pages 243--260, 2006. Google ScholarDigital Library
- J.-G. Lee, J. Han, and X. Li. Trajectory outlier detection: A partition-and-detect framework. In Proc. ICDE 2008, pages 140--149, 2008. Google ScholarDigital Library
- B. Li, D. Zhang, L. Sun, C. Chen, S. Li, G. Qi, and Q. Yang. Hunting or waiting? discovering passenger-finding strategies from a large-scale real-world taxi dataset. In MUCS'11 in conjunction with PerCom 2011, pages 63--68, 2011.Google ScholarCross Ref
- X. Li, J. Han, S. Kim, and H. Gonzalez. ROAM: Rule- and motif-based anomaly detection in massive moving object data sets. In Proc. SDM 2007, pages 273--284, 2007.Google ScholarCross Ref
- X. Li, Z. Li, J. Han, and J.-G. Lee. Temporal outlier detection in vehicle traffic data. In Proc. ICDE 2009, pages 1319--1322, 2009. Google ScholarDigital Library
- L. Liao, D. J. Patterson, D. Fox, and H. Kautz. Learning and inferring transportation routines. Artificial Intelligence, 171(5-6):311--331, 2007. Google ScholarDigital Library
- Z. Liao, Y. Yu, and B. Chen. Anomaly detection in GPS data based on visual analytics. In Proc. VAST 2010, pages 51--58, 2010.Google ScholarCross Ref
- F. T. Liu, K. M. Ting, and Z.-H. Zhou. Isolation forest. In Proc. ICDM 2008, pages 413--422, 2008. Google ScholarDigital Library
- L. Liu, C. Andrisa, and C. Rattia. Uncovering cabdrivers' behaviour patterns from their digital traces. Computers, Environment and Urban Systems, 34(6):541--548, 2010.Google ScholarCross Ref
- D. J. Patterson, L. Liao, D. Fox, and H. A. Kautz. Inferring high-level behavior from low-level sensors. In Proc. Ubicomp 2003, pages 73--89, 2003.Google ScholarCross Ref
- S. Phithakkitnukoon, M. Veloso, C. Bento, A. Biderman, and C. Ratti. Taxi-aware map: Identifying and predicting vacant taxis in the city. In Proc. AmI 2010, pages 86--95, 2010. Google ScholarDigital Library
- G. Qi, X. Li, S. Li, G. Pan, and D. Zhang. Measuring social functions of city regions from large-scale taxi behaviors. In Proc. PerCom 2011, pages 21--25, 2010.Google Scholar
- R. R. Sillito and R. B. Fisher. Semi-supervised learning for anomalous trajectory detection. In Proc. BMVC 2008, pages 1035--1044, 2008.Google ScholarCross Ref
- J. Yuan, Y. Zheng, C. Zhang, W. Xie, X. Xie, G. Sun, and Y. Huang. T-drive: Driving directions based on taxi trajectories. In Proc. GIS 2010, pages 99--108, 2010. Google ScholarDigital Library
- D. Zhang, B. Guo, and Z. Yu. The emergence of social and community intelligence. IEEE-Computer, July:21--28, 2011. Google ScholarDigital Library
- V. W. Zheng, B. Cao, Y. Zheng, X. Xie, and Q. Yang. Collaborative filtering meets mobile recommendation: A user-centered approach. In Proc. AAAI 2010, 2010.Google Scholar
- Y. Zheng, L. Zhang, X. Xie, and W.-Y. Ma. Mining interesting locations and travel sequences from GPS trajectories. In Proc. WWW 2009, pages 791--800, 2009. Google ScholarDigital Library
- B. D. Ziebart, A. L. Maas, A. K. Dey, and J. A. Bagnell. Navigate like a cabbie: probabilistic reasoning from observed context-aware behavior. In Proc. Ubicomp 2008, pages 322--331, 2008. Google ScholarDigital Library
- J. Zobel and A. Moffat. Inverted files for text search engines. ACM Computing Surveys, 38(2), 2006. Google ScholarDigital Library
Index Terms
- iBAT: detecting anomalous taxi trajectories from GPS traces
Recommendations
Real Time Anomalous Trajectory Detection and Analysis
GPS-equipped taxis can be considered as pervasive sensors and the large-scale digital traces produced allow us to reveal many hidden facts about the city dynamics and human behaviors. In this paper we present a novel GPS-based taxi system which can ...
Taxi Route Recommendation Based on Urban Traffic Coulomb’s Law
Web Information Systems Engineering – WISE 2017AbstractWith the advances and availability of networking and data processing technologies, the number of researches supporting taxi as a mean of transportation and further optimization of their route selection is increasing and broadly discussed. For the ...
On Ridesharing Competition and Accessibility: Evidence from Uber, Lyft, and Taxi
WWW '18: Proceedings of the 2018 World Wide Web ConferenceRidesharing services such as Uber and Lyft have become an important part of the Vehicle For Hire (VFH) market, which used to be dominated by taxis. Unfortunately, ridesharing services are not required to share data like taxi services, which has made it ...
Comments