This study investigates the relationship between the introduction of ride-hailing services and 1) taxi trips; 2) AirTrain ridership; and 3) parking transactions at two major New York airports: John F. Kennedy International Airport (JFK) and LaGuardia Airport (LGA). Using monthly data that span over a decade, this study applies Bayesian structural time series analysis to examine how taxi trips, AirTrain ridership, and parking transactions have changed since the launch of UberX and Lyft, two of the most popular ride-hailing services in New York City. Results suggest that after the launch of the ride-hailing services, the number of taxi trips (both for drop-offs and pick-ups) decreased at both airports from what it would have been in the absence of ride-hailing services, with the decline being more significant for drop-offs. In contrast, AirTrain ridership continued its growing trend after the launch of UberX and Lyft. The number of parking transactions at JFK continued the mild decline that began before the launch of UberX and Lyft. Our findings indicate that high-quality transit services that connect airports with city centers and intermodal terminals could continue to serve as a crucial airport access mode amid the popularity of ride-hailing services. The findings also highlight the mismatch between taxi drop-offs and pick-ups at the airport and the potential revenue loss from parking decline. The contributions of this study are also methodological, as our study demonstrates the application of a novel statistical method to study the effect of an intervention such as ride-hailing services.
Get full access to this article
View all access options for this article.
References
1.
GiulianoG.GordonP.PanQ.ParkJ.WangL.Estimating Freight Flows for Metropolitan Area Highway Networks using Secondary Data Sources. Networks and Spatial Economics, Vol. 10, No. 1, 2010, pp. 73–91. https://doi.org/10.1007/s11067-007-9024-9.
FuellhartK.OomsK.DerudderB.O’ConnorK.Patterns of US Air Transport across the Economic Unevenness of 2003–2013. Journal of Maps, Vol. 12, No. 5, 2016, pp. 1253–1257. https://doi.org/10.1080/17445647.2016.1152917.
4.
National Academies of Sciences, Engineering, and Medicine; Transportation Research Board; Airport Cooperative Research Program; P. Mandle, and S. Box. ACRP Synthesis 84: Transportation Network Companies: Challenges and Opportunities for Airport Operators. Transportation Research Board of the National Academies, Washington, D.C., 2017.
Port Authority of New York and New Jersey. The Port Authority of NY & NJ December 2018 Traffic Report: JFK. Port Authority of New York and New Jersey, NY, 2019.
11.
Port Authority of New York and New Jersey. The Port Authority of NY & NJ September 2019 Traffic Report: LGA. Port Authority of New York and New Jersey, NY, 2019.
de CerreñoA. L. C.Integrated Transportation and Land Use Planning: Facilitating Coordination Across and Among Jurisdictions. Rudin Center for Transportation Policy & Management, New York, NY, 2008.
18.
PelsE.NijkampP.RietveldP.Access to and Competition between Airports: A Case Study for the San Francisco Bay Area. Transportation Research Part A: Policy and Practice, Vol. 37, No. 1, 2003, pp. 71–83. https://doi.org/10.1016/S0965-8564(02)00007-1.
19.
GuptaS.VovshaP.DonnellyR.Air Passenger Preferences for Choice of Airport and Ground Access Mode in the New York City Metropolitan Region. Transportation Research Record: Journal of the Transportation Research Board, 2008. 2042: 3–11.
20.
SperryB. R.LarsonS.LeucingerD.JanowiakS.MorganC. A.Intercity Passenger Rail Access to Airports: Case Study in Milwaukee, Wisconsin. Transportation Research Record: Journal of the Transportation Research Board, 2012. 2300: 22–30.
21.
MandleP. B.ManselD. M.CooganM. A.Use of Public Transportation by Airport Passengers. Transportation Research Record: Journal of the Transportation Research Board, 2000. 1703: 83–89.
22.
RyersonM. S.KimA. M.A Drive for Better Air Service: How Air Service Imbalances across Neighboring Regions Integrate Air and Highway Demands. Transportation Research Part A: Policy and Practice, Vol. 114, 2018, pp. 237–255. https://doi.org/10.1016/j.tra.2017.10.005.
23.
RayleL.DaiD.ChanN.CerveroR.ShaheenS.Just a Better Taxi? A Survey-Based Comparison of Taxis, Transit, and Ridesourcing Services in San Francisco. Transport Policy, Vol. 45, 2016, pp. 168–178. https://doi.org/10.1016/j.tranpol.2015.10.004.
24.
ContrerasS. D.PazA.The Effects of Ride-Hailing Companies on the Taxicab Industry in Las Vegas, Nevada. Transportation Research Part A: Policy and Practice, Vol. 115, 2018, pp. 63–70. https://doi.org/10.1016/j.tra.2017.11.008.
25.
NieY. M.How can the Taxi Industry Survive the Tide of Ridesourcing? Evidence from Shenzhen, China. Transportation Research Part C: Emerging Technologies, Vol. 79, 2017, pp. 242–256. https://doi.org/10.1016/j.trc.2017.03.017.
26.
YoungM.FarberS.The Who, Why, and When of Uber and Other Ride-Hailing Trips: An Examination of a Large Sample Household Travel Survey. Transportation Research Part A: Policy and Practice, Vol. 119, 2019, pp. 383–392. https://doi.org/10.1016/j.tra.2018.11.018.
CramerJ.KruegerA. B.Disruptive Change in the Taxi Business: The Case of Uber. The American Economic Review, Vol. 106, No. 5, 2016, pp. 177–182.
29.
BoisjolyG.GriséE.MaguireM.VeilletteM.-P.DeboosereR.BerrebiE.El-GeneidyA.Invest in the Ride: A 14 Year Longitudinal Analysis of the Determinants of Public Transport Ridership in 25 North American Cities. Transportation Research Part A: Policy and Practice, Vol. 116, 2018, pp. 434–445. https://doi.org/10.1016/j.tra.2018.07.005.
30.
ClewlowR. R.MishraG. S.Disruptive Transportation: The Adoption, Utilization, and Impacts of Ride-Hailing in the United States. University of California, Davis, CA, 2017, p. 35.
31.
HallJ. D.PalssonC.PriceJ.Is Uber a Substitute or Complement for Public Transit?Journal of Urban Economics, Vol. 108, 2018, pp. 36–50. https://doi.org/10.1016/j.jue.2018.09.003.
32.
GraehlerM., Jr.MucciR. A.ErhardtG. D.Understanding the Recent Transit Ridership Decline in Major US Cities: Service Cuts or Emerging Modes?Presented at 98th Annual Meeting of the Transportation Research Board, Washington, D.C., 2019.
33.
HampshireR. C.SimekC.FabusuyiT.DiX.ChenX.Measuring the Impact of an Unanticipated Disruption of Uber/Lyft in Austin, TX, 2017. http://dx.doi.org/10.2139/ssrn.2977969.
34.
HermawanK.ReganA. C.Impacts on Vehicle Occupancy and Airport Curb Congestion of Transportation Network Companies at Airports. Transportation Research Record: Journal of the Transportation Research Board, 2018. 2672: 52–58.
35.
WadudZ.An Examination of the Effects of Ride-Hailing Services on Airport Parking Demand. Journal of Air Transport Management, Vol. 84, 2020, p. 101783.
36.
de VochtF. Inferring the 1985–2014 Impact of Mobile Phone Use on Selected Brain Cancer Subtypes using Bayesian Structural Time Series and Synthetic Controls. Environment International, Vol. 97, 2016, pp. 100–107. https://doi.org/10.1016/j.envint.2016.10.019.
37.
McQuireC.TillingK.HickmanM.de VochtF.Forecasting the 2021 Local Burden of Population Alcohol-Related Harms using Bayesian Structural Time–Series. Addiction, Vol. 114, No. 6, 2019, pp. 994–1003. https://doi.org/10.1111/add.14568.
38.
FullerD.LuanH.BuoteR.AuchinclossA. H.Impact of a Public Transit Strike on Public Bicycle Share Use: An Interrupted Time Series Natural Experiment Study. Journal of Transport & Health, Vol. 13, 2019, pp. 137–142. https://doi.org/10.1016/j.jth.2019.03.018.
BrodersenK. H.GallusserF.KoehlerJ.RemyN.ScottS. L.Inferring Causal Impact using Bayesian Structural Time-Series Models. The Annals of Applied Statistics, Vol. 9, No. 1, 2015, pp. 247–274.
41.
ScottS. L.VarianH. R.Predicting the Present with Bayesian Structural Time Series. International Journal of Mathematical Modelling and Numerical Optimisation, Vol. 5, No. 1–2, 2014, pp. 4–23. https://doi.org/10.1504/IJMMNO.2014.059942.
42.
QiuJ.JammalamadakaS. R.NingN.Multivariate Bayesian Structural Time Series Model. Journal of Machine Learning Research, Vol. 19, No. 68, 2018, pp. 1–33.
43.
HinneM.GronauQ. F.van den BerghD.WagenmakersE.-J.A Conceptual Introduction to Bayesian Model Averaging. https://doi.org/10.31234/osf.io/wgb64. Accessed April 3, 2020.
44.
HoetingJ. A.MadiganD.RafteryA. E.VolinskyC. T.Bayesian Model Averaging: A Tutorial. Statistical Science, Vol. 14, No. 4, 1999, pp. 382–401.
LaneB. W.A Time-Series Analysis of Gasoline Prices and Public Transportation in US Metropolitan Areas. Journal of Transport Geography, Vol. 22, 2012, pp. 221–235. https://doi.org/10.1016/j.jtrangeo.2011.10.006.
50.
NowakW. P.SavageI.The Cross Elasticity between Gasoline Prices and Transit Use: Evidence from Chicago. Transport Policy, Vol. 29, 2013, pp. 38–45. https://doi.org/10.1016/j.tranpol.2013.03.002.
