Modeling Taxi Trip Demand by Time of Day in New York City
Abstract
Identifying the factors that influence taxi demand is very important for understanding where and when people use taxis. A large set of GPS data from New York City taxis is used along with demographic, socioeconomic, and employment data to identify the factors that drive taxi demand. A technique was developed to measure and map transit accessibility on the basis of transit access time (TAT) to understand the relationship between taxi use and transit service. The taxi data were categorized by pickups and drop-offs at different times of day. A multiple linear regression model was estimated for each hour of the day to model pickups and another to model drop-offs. Six important explanatory variables that influence taxi trips were identified: population, education, age, income, TAT, and employment. The influence of these factors on taxi pickups and drop-offs changed at different times of the day. The number of jobs in each industry sector was an indication of the types of economic activities occurring at a location, and in some sectors the number of jobs were strongly associated with taxi use. This study demonstrates the temporal and spatial variation of taxi demand and shows how transit accessibility and other factors affect it.
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