Date of Completion

8-10-2018

Embargo Period

2-5-2019

Advisors

Karthik Konduri, Nalini Ravishanker, Nicholas Lownes

Field of Study

Civil Engineering

Degree

Master of Science

Open Access

Open Access

Abstract

The concept of shared travel, or making trips with other users via a common vehicle, is far from novel. However, recent technological advances have paved the way for new mobility alternatives within established transportation networks; including on-demand ride hailing/sharing/networking (e.g. Uber, Lyft) and citywide bike sharing systems. This shared concept has flourished and is being hailed as a potential option for autonomous vehicle operation moving forward. However, substantial investigation into how shared mode offerings impact travel behaviors and integrate into existing transportation networks is lacking. To address this, this research explores two main investigations. First, understand the demand for one transportation networking company (TNC), specifically Uber, at a neighborhood level. Second, explore how growing TNC demand is impacting the demand for all other shared modes, including taxi, bikeshare, and subway, at a citywide scale. To accomplish this, openly available weekly demand data on New York City is analyzed at the taxi zone level in Manhattan from April-September 2014 and January-June 2015 using a panel based random effects model. In addition, daily data from January 2015-June 2017 on Uber, Lyft, Via, Taxi, City Bike, and Subway are explored using a Dynamic Linear Modeling (DLM) framework. Using data from 2014-2015, it is apparent that the demand for TNCs is growing substantially, but in heavily residential neighborhoods there is evidence for more limited growth or a potentially stagnation in the growth. Looking across modes from 2015 to 2017, daily TNC demand within NYC grew by over 200% while the taxi daily demand fell by about 20% and City Bike grew by around 40%. The subway demand remained relatively constant with a slight decline during weekends. As a preliminary work, the potential relationships across demand are discussed and used to motivate future investigations.

Major Advisor

Karthik Konduri

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