Mode Substitution of Emerging Mobility Services
Mode Substitutional Patterns of Ridehailing and Micro-mobility Services
Principal Investigator: Giovanni Circella, Senior Research Engineer, School of Civil and Environmental Engineering
Co-principal Investigator: Patricia L. Mokhtarian, Clifford and William Greene, Jr. Professor, School of Civil and Environmental Engineering
Project Duration: 12 months
Project Budget (Federal UTC Funds): $174,018
Project Budget (Cost-share): $83,666
Institution: Georgia Institute of Technology
Abstract
In this study, we explore the heterogeneous impacts of ridehailing on the use of other travel modes using survey data (N = 1,438) collected from June to October 2019 (i.e., before the COVID-19 pandemic) across three regions in southern U.S. states: Phoenix, Arizona; Atlanta, Georgia; and Austin, Texas. We apply a latent-class cluster analysis to indicators of changes in the use of various travel modes as a result of ridehailing adoption, with covariates of socioeconomics, demographics, a land-use attribute, and individual attitudes. We identify four distinctive latent classes of behavioral changes in response to the use of ridehailing. About half of ridehailing users in the sample (49.7%) are found to behave as Mobility augmenters, who use ridehailing rarely, in addition to other travel modes, and do not change their travel routines much as a result of the adoption of this mobility service. The second largest class includes Exogenous changers (24.5%), whose members report many changes in their use of various travel modes, but which can be largely explained by other reasons. Private car/taxi substituters (15%) frequently hail a ride, and as a result, reduce their use of private vehicles while making more trips by public transit and active modes, as the result of using ridehailing. Interestingly, Transit/active mode substituters (10.8%) often use ridehailing, likely for trips that they previously made by public transit or active modes, and consequently reduce their use of these less-polluting modes while enjoying enhanced mobility. This study reveals substantial heterogeneity in ridehailing impacts, which were masked in previous studies that focused on average impacts, and it suggests that policy responses should be customized by users’ socioeconomics and residential neighborhoods.