Indexing the positions of continuously moving objects
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Capturing the Uncertainty of Moving-Object Representations
SSD '99 Proceedings of the 6th International Symposium on Advances in Spatial Databases
Managing uncertainty in moving objects databases
ACM Transactions on Database Systems (TODS)
Querying Imprecise Data in Moving Object Environments
IEEE Transactions on Knowledge and Data Engineering
Index for fast retrieval of uncertain spatial point data
GIS '06 Proceedings of the 14th annual ACM international symposium on Advances in geographic information systems
Optimal matching between spatial datasets under capacity constraints
ACM Transactions on Database Systems (TODS)
Where to find my next passenger
Proceedings of the 13th international conference on Ubiquitous computing
Towards reducing taxicab cruising time using spatio-temporal profitability maps
SSTD'11 Proceedings of the 12th international conference on Advances in spatial and temporal databases
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
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Thousands of taxis cruise a metropolitan road network looking for passengers that may be scattered or clustered in highly active locations. Taxicab drivers tend to gravitate to the known clusters, often leading to supply and demand disequilibrium as areas become under or over served. Many cities monitor their taxi fleet's locations using GPS devices and track passenger occupancy through trip meters, thereby producing data streams of taxicab trajectories and passenger activities. This paper presents the Service Disequilibrium Detection (SDD) framework which aims at identifying regions of service disequilibrium using this information. The SDD framework models request wait time and taxicab location uncertainty inherent in the discrete data streams and identifies the disequilibrium regions using two methods: (1) Bayesian spatial scan statistics, and (2) Poisson-based hypothesis testing. We claim the SDD framework can detect emerging disequilibrium and validate this claim using a large Shanghai taxi GPS data set.