Forecasting Cybercar Use for Airport Ground Access: Case Study at Baltimore Washington International Airport

Abstract

Nested logit models calibrated on revealed preference (RP) and stated preference (SP) data have been used in this paper to study the potential market of a cybernetic transportation system for airport ground access. This innovative concept of transit mode could complement mass transit and nonmotorized transport modes, providing passenger service for any location at any time. Our analysis is based on a data set collected at Baltimore Washington Thurgood Marshall International (BWI) airport; the proposed cybercar service was designed to connect the Greenbelt metro station to the airport. The explanatory variables used in the utility functions of the RP/SP joint models are level of service attributes, characteristic specific to the new service offered, and socioeconomic variables. The estimated models are used to predict the market share of automobile, taxi, cybercar, and modified transit. In addition, elasticity tests are performed in order to study individuals’ sensitivity to the change of travel time and travel cost for automobile and cybercar. The estimation and application results in this paper can be used as a reference for the CityMobil program of the European Union to popularize this fast, convenient, and environmentally friendly cybercar. Meanwhile, the results provide useful behavioral data and models on the potential of advanced technological transportation system for airport ground planning in the United States.