Modeling Taxi Demand and Supply in New York City Using Large-Scale Taxi GPS Data
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 conven
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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. Keywords Big data • Taxi demand modeling • Taxi GPS data • Transit accessibility • Count regression model
1 Introduction Spatially referenced big data provides opportunities to obtain new and useful insights on transportation markets in large urban areas. One such source is the set of trip records that are collected and logged using in-vehicle Global Positioning
C. Yang, Ph.D (*) Senior Transportation Data Scientist, DIGITALiBiz, Inc., 55 Broadway, Cambridge, MA 02142, USA e-mail: [email protected] E.J. Gonzales, Ph.D. (*) Department of Civil and Environmental Engineering, University of Massachusetts Amherst, 130 Natural Resources Road, Amherst, MA 01003, USA e-mail: [email protected] © Springer International Publishing Switzerland 2017 P. Thakuriah et al. (eds.), Seeing Cities Through Big Data, Springer Geography, DOI 10.1007/978-3-319-40902-3_22
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Systems (GPS) in taxicab fleets. In large cities, tens of thousands of records are collected every day, amounting to data about millions of trips per year. The raw data sets are too large to analyze with conventional tools, and the insights that are gained from looking at descriptive statistics or visualizations of individual vehicle trajectories are limited. A great opportunity exists to improve our understanding of transportation in cities and the specific role of the taxicab market within the transportation system by processing and integrating the data with a Geographic Information System (GIS). Moving beyond simple descriptions and categorizations of the taxi trip data, the development of sophisticated models and visualizations of vehicle movements and demand patterns can provide insights about the nature of urban travel demand, the performance of the street network, and operation of the taxicab fleet that uses it. Taxicabs are an important mode of public transportation in many urban areas, providing
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