Modeling taxi demand and supply in New York city using large-scale taxi GPS data

Abstract

Data from taxicabs equipped with Global Positioning Systems (GPS) are collected by many transportation agencies, including the Taxi and Limousine Commission in New York City. The raw data sets are too large and complex to analyze directly with many conventional tools, but when the big data are appropriately processed and integrated with Geographic Information Systems (GIS), sophisticated demand models and visualizations of vehicle movements can be developed. These models are useful for providing insights about the nature of travel demand as well as the performance of the street network and the fleet of vehicles that use it. This paper demonstrates how big data collected from GPS in taxicabs can be used to model taxi demand and supply, using 10 months of taxi trip records from New York City. The resulting count models are used to identify locations and times of day when there is a mismatch between the availability of taxicabs and the demand for taxi service in the city. The findings are useful for making decisions about how to regulate and manage the fleet of taxicabs and other transportation systems in New York City.