AbstractWith the rapidly changing landscape for taxis, ride-hailing, and ride-sourcing services, public agencies have an urgent need to understand how such new services impact social welfare: impacts of technologies on matching customers to service providers, evaluating ride-sourcing operations, and evaluating surge pricing policy, among others. We conduct an empirical study to answer this question for Uber using a dynamic spatial equilibrium taxi-matching model. Given a matching function, the spatial distribution of demand activities, and service coverage, the model outputs equilibrium fleet sizes, matches, and social welfare by zone and time of day. Uber provides pickup data for a specific time period in New York City (NYC). Parameters from the model calibrated from medallion cab (Taxi) data are grafted onto the Uber model to supplement the missing information. The Uber model has a root-mean square error of 7.75 matches/zone/interval, which is approximately an 8.52% error. Spatial distribution of responses in demand to fare hikes or vehicle supply to demand surges measurably differ between NYC Taxi and Uber markets. Baseline estimations of welfare indicate that the NYC Taxi industry generates $495,900 in consumer surplus and $1,022,400 in Taxi profits for the 4-h interval, while for the Uber market, the model estimates $73,300 in consumer surplus and $151,300 in Uber profits during the same interval. Spatial-temporal dynamics resulting from fare hike and congestion fee scenarios are analyzed to determine requirements for allocating the congestion charge revenues toward public transit to maintain or improve upon the same consumer surplus.

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