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原文传递 Spatial-Dynamic Matching Equilibrium Models of New York City Taxi and Uber Markets

题名：	Spatial-Dynamic Matching Equilibrium Models of New York City Taxi and Uber Markets
正文语种：	eng
作者：	Correa, Diego;Chow, Joseph Y. J.;Ozbay, Kaan
作者单位：	NYU C2SMART Ctr Tandon Sch Engn Dept Civil & Urban Engn 6 Metrotech Ctr Brooklyn NY 11201 USA\|Catholic Univ Cuenca Acad Unit Engn Ind & Construct Sch Civil Engn Av Amer & Gen Torres Cuenca 010101 Ecuador;NYU C2SMART Ctr 6 Metrotech Ctr Brooklyn NY 11201 USA\|NYU Tandon Sch Engn Dept Civil & Urban Engn 6 Metrotech Ctr Brooklyn NY 11201 USA;NYU C2SMART Ctr Dept Civil & Urban Engn 6 Metrotech Ctr Brooklyn NY 11201 USA\|NYU Tandon Sch Engn Ctr Urban Sci & Progress 6 Metrotech Ctr Brooklyn NY 11201 USA
关键词：	Uber and taxi demand;Matching models;Spatial dynamic equilibrium;Spatial distribution;Social welfare
摘要：	With 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.
出版年：	2021
期刊名称：	Journal of Transportation Engineering
卷：	147
期：	9