Real-time taxi dispatching using Global Positioning Systems
Communications of the ACM - Wireless networking security
Inverted files for text search engines
ACM Computing Surveys (CSUR)
Navigate like a cabbie: probabilistic reasoning from observed context-aware behavior
UbiComp '08 Proceedings of the 10th international conference on Ubiquitous computing
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
Efficient anomaly monitoring over moving object trajectory streams
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
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
Urban mobility study using taxi traces
Proceedings of the 2011 international workshop on Trajectory data mining and analysis
Proceedings of the 13th international conference on Ubiquitous computing
iBAT: detecting anomalous taxi trajectories from GPS traces
Proceedings of the 13th international conference on Ubiquitous computing
Where to find my next passenger
Proceedings of the 13th international conference on Ubiquitous computing
A Taxi Driving Fraud Detection System
ICDM '11 Proceedings of the 2011 IEEE 11th International Conference on Data Mining
From taxi GPS traces to social and community dynamics: A survey
ACM Computing Surveys (CSUR)
Hi-index | 0.00 |
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 detect ongoing anomalous passenger delivery behaviors leveraging our proposed iBOAT method. To achieve real time monitoring, we reduce the response time of iBOAT by more than five times with an inverted index mechanism adopted. We evaluate the effectiveness of the system with large scale real life taxi GPS records while serving 200,000 taxis. With this system, we obtain about 0.44 million anomalous trajectories out of 7.35 million taxi delivery trips, which correspond to 7600 taxis' GPS records in one month time in the city of Hangzhou, China. Through further analysis of these anomalous trajectories, we observe that: (1) Over 60 % of the anomalous trajectories are "detours" that travel longer distances and time than normal trajectories; (2) The average trip length of drivers with high-detour tendency is 20 % longer than that of normal drivers; (3) The length of anomalous sub-trajectories is usually less than a third of the entire trip, and they tend to begin in the first two thirds of the journey; (4) Although longer distance results in a greater taxi fare, a higher tendency to take anomalous detours does not result in higher monthly revenue; and (5) Taxis with a higher income usually spend less time finding new passengers and deliver them in faster speed.