Detecting regions of disequilibrium in taxi services under uncertainty

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.