A big data driven model for taxi drivers´ airport pick-up decisions in New York City

Taxis play a vital role in airport ground transportation in terms of local and regional accessibility to and from the city. Taxi drivers´ decisions to make airport trips are one of the most important factors that maintain taxi demand and supply equilibrium at the airports. In this paper, a large taxi trip dataset is used to model New York City taxi drivers´ decision process in order to suggest policies for improving John F. Kennedy (JFK) Airport ground access and passenger satisfaction. For this purpose, sequential taxi trips of drivers are identified, and the binary decision of “airport pick-up” or “cruising for customers” at the end of each trip is modeled using logistic regression. The model provides an important tool which can be used to suggest and assess policy recommendations for improving taxi operations at JFK. Considering the increasing availability of taxi trip records in the world, the suggested methodology can also be applied elsewhere.