Comparison of Mode Cost by Time of Day for Nondriving Airport Trips to and from New York City's Pennsylvania Station

Abstract

A novel methodology used taxi global position system data and high-resolution transit schedule information to compare travel times and travel fares of the two main nondriving travel modes for airport ground access: taxi and transit. Five origin–destination pairs between Pennsylvania Station in New York City and three airports in the New York region were used as an example to demonstrate these methods. An analysis of total trip cost considered both travel time and expenditures on fare. A binary logit model was used to model the mode choice of travelers. The results indicate that transit is the more likely choice during most of the day except the midnight period when transit service has longer headways. A sensitivity analysis shows the relationship between the value of time and total trip cost per passenger for different numbers of passengers traveling together and at different times of day. The higher the value of time and the number of passengers in a group, the more likely it is that a taxi is chosen for airport trips. The attractiveness of one mode relative to the other varies spatially and temporally according to the travel time and price. This paper focuses on understanding temporal variation of total cost of each mode and the effect that this variation is likely to have on mode share.

Exploring the Taxi and Uber Demands in New York City: An Empirical Analysis and Spatial Modeling

“…Other studies that utilized NYC taxi trip data estimated a binary logit model to model the mode choice between transit and taxi modes (15), compared trip characteristics between summer (July) and non-summer (March) months (16), and developed a data visualization tool namely, TaxiVis, which is a software implementation that allows users to visually query taxi trips by considering spatial, temporal, and other constraints (17).…”

Spatiotemporal Pattern Analysis of Taxi Trips in New York City

“…Other analysis tasks that used New York City taxi trip data provided by TLC have included the development of a binary logit model to predict the mode choice between transit and taxi mode (14); comparison of trip characteristics between summer (July) and nonsummer (March) months (15); and the development of TaxiVis, which is an analysis environment that allows users to visually query taxi trips under consideration of spatial, temporal, and attribute constraints (16).…”