Impact of Transportation Network Companies on Ground Access to Airports: A Case Study in Austin, Texas

This study proposes using intelligent transportation systems (ITS) and open-data sources to evaluate the impact of transportation network companies (TNCs) on ground access to airports. The unexpected interruption of the TNCs services in Austin, Texas, U.S., in 2016, is used as a natural experiment to provide a before-and-after analysis of the changes in the traffic conditions of the access area to the Austin-Bergstrom International Airport (ABIA). An analysis of variance (ANOVA) is implemented to determine whether the difference in speeds across periods is statistically significant, and the value of time for TNC-induced delay is estimated, using values of passengers’ willingness to pay for airport access travel time savings. Furthermore, a speed linear model is developed to assess the impact of TNC demand on ground access areas using trip information from an Austin-based TNC service. The main results suggest that airport ground access speeds were higher during the period that the TNCs were out of the city. The re-introduction of the services resulted in a speed reduction of 9% for the airport morning and 18% for the afternoon peak hours, translating to a total passenger cost of approximately $150+ (morning) and $400+ (afternoon) per hour. Furthermore, it was found that the number of TNC pick-up trips is a predictor of airport access speed and that the flight schedule can potentially be used to develop predictive speed models.

Joint Modeling of Access Mode and Parking Choice of Air Travelers Using Revealed Preference Data

Airport ground access mode choice is distinct from everyday mode choice decisions, necessitating context-specific choice model estimation. Understanding airport ground access mode choice decisions is not only important for developing infrastructure planning strategies, but also for assessing the impacts of emerging modes on airport revenues, particularly from parking. However, parking choice is an often-overlooked dimension in airport ground access choice modeling. This paper addresses this gap through the development of a joint model of airport access mode and parking option choice using a passenger survey conducted at Dallas-Fort Worth (DFW) International Airport in 2015. Compared with a traditional conditional logit model that does not consider parking options available at DFW airport, the joint model of mode and parking decisions was found to generate more realistic values of travel time and was shown to have better predictive performance, both of which are critical for obtaining better airport parking revenue estimates and identifying traveler cohorts who may respond more strongly to potential policies targeting curb congestion and parking demand.

Taxi Drops Off as Transit Grows amid Ride-Hailing’s Impact on Airport Access in New York

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.