Impacts of Ridesourcing - Lyft and Uber - on Transportation Including VMT, Mode Replacement, Parking, and Travel Behavior

Abstract

The transportation sector is currently experiencing a disruption with the introduction and evolution of technology and transportation services such as bikesharing, carsharing, on-demand ridesourcing (e.g. Lyft, Uber), and microtransit (e.g. Bridj, Chariot). As these new layers of technology-based transportation options begin to flourish, it is important to understand how they affect our transportation systems and society. This doctoral dissertation analyzes the impacts of ridesourcing on several areas of transportation including: efficiency in terms of distance – Vehicles Miles Traveled (VMT) versus Passenger Miles Traveled (PMT) – and travel times, mode replacement, VMT increase, parking, transportation equity, and travel behavior. Realizing the difficulty in obtaining data directly from Lyft and Uber, this research employs an innovative approach by the author becoming an independent contractor to drive for both companies; this allowed the author to gain access to exclusive data and real-time passenger feedback. The datasets include actual travel attributes – such as times, distances, and earnings – from 416 rides (Lyft, UberX, LyftLine, and UberPool), and travel behavior and socio-demographics from 311 passenger interviews. This dissertation estimates a low ridesourcing efficiency rate compared to other modes, mix of modes replacement, overall increase in VMT, decrease in parking demand, low wages (i.e. net earnings) for drivers, travel behavior changes for users, as well as relationships between modality style, trip purpose, and stated reasons for mode replacement. These results give us insights into the impacts of ridesourcing on several key aspects of transportation. This, in turn, will help cities and transportation organizations better account for ridesourcing in their planning and engineering processes (e.g. travel demand models) as well as policy decisions.