Lazy learning
LOF: identifying density-based local outliers
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Distance-based outliers: algorithms and applications
The VLDB Journal — The International Journal on Very Large Data Bases
Discovering cluster-based local outliers
Pattern Recognition Letters
Inverted files for text search engines
ACM Computing Surveys (CSUR)
Outlier detection by active learning
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Learning and inferring transportation routines
Artificial Intelligence
Navigate like a cabbie: probabilistic reasoning from observed context-aware behavior
UbiComp '08 Proceedings of the 10th international conference on Ubiquitous computing
DOLPHIN: An efficient algorithm for mining distance-based outliers in very large datasets
ACM Transactions on Knowledge Discovery from Data (TKDD)
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
Mining interesting locations and travel sequences from GPS trajectories
Proceedings of the 18th international conference on World wide web
Trajectory Outlier Detection: A Partition-and-Detect Framework
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Temporal Outlier Detection in Vehicle Traffic Data
ICDE '09 Proceedings of the 2009 IEEE International Conference on Data Engineering
Efficient anomaly monitoring over moving object trajectory streams
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
From GPS traces to a routable road map
Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
T-drive: driving directions based on taxi trajectories
Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems
Top-Eye: top-k evolving trajectory outlier detection
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Taxi-aware map: identifying and predicting vacant taxis in the city
AmI'10 Proceedings of the First international joint conference on Ambient intelligence
Predestination: inferring destinations from partial trajectories
UbiComp'06 Proceedings of the 8th international conference on Ubiquitous Computing
Prediction of urban human mobility using large-scale taxi traces and its applications
Frontiers of Computer Science in China
Proceedings of the ACM SIGKDD International Workshop on Urban Computing
Proceedings of the ACM SIGKDD International Workshop on Urban Computing
Mining the semantics of origin-destination flows using taxi traces
Proceedings of the 2012 ACM Conference on Ubiquitous Computing
Exploring social properties in vehicular ad hoc networks
Proceedings of the Fourth Asia-Pacific Symposium on Internetware
Data-driven study of urban infrastructure to enable city-wide ubiquitous computing
Proceedings of the 2nd International Workshop on Big Data, Streams and Heterogeneous Source Mining: Algorithms, Systems, Programming Models and Applications
Real Time Anomalous Trajectory Detection and Analysis
Mobile Networks and Applications
coRide: carpool service with a win-win fare model for large-scale taxicab networks
Proceedings of the 11th ACM Conference on Embedded Networked Sensor Systems
Crowd sensing of traffic anomalies based on human mobility and social media
Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
From taxi GPS traces to social and community dynamics: A survey
ACM Computing Surveys (CSUR)
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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 (AUC0.99, 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.