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Please use this identifier to cite or link to this item: http://arks.princeton.edu/ark:/88435/dsp01p2676x91d
Title: New York City Taxicab Transportation Demand Modeling for the Analysis of Ridesharing and Autonomous Taxi Systems
Authors: Swoboda, Andrew James
Advisors: Kornhauser, Alain
Department: Operations Research and Financial Engineering
Class Year: 2015
Abstract: A major contributor to the development of society is its capacity for rapid transportation. As this technological capability improves and spreads, the world shrinks and becomes increasingly accessible. Countries, governments, economies, and demographics all change as a result of a more efficient and connected society. The apparent fact that daily transportation systems appear to have plateaued in their expeditiousness begs the question of whether there are more efficient methods of transportation within the confines of the current system. It is important to intelligently analyze the existing transportation network to determine manners in which automobiles can optimally coexist and commute. By implementing more sophisticated and efficient transportation systems on today’s existing network of roads, the United States will come closer to achieving various national and societal goals that have been in sight for decades: first, an improved national transportation system will reduce the amount of unnecessarily wasted fuel, shrinking both the country’s dependence on foreign oil and its carbon footprint; second, a more intelligent network will improve the quality of life of Americans, as it will decrease congestion and increase access to mobility as a result of lower costs for personal transportation; lastly, a “smarter” and decreased amount of congestion will result in a safer transportation experience. Within the Operations Research and Financial Engineering department at Princeton University, Professor Alain Kornhauser has been conducting ongoing research to accurately model national transportation demand. Upon establishing an accurate model of daily trips, the feasibility and potential applications of ridesharing on a national scale can be determined in an attempt to reduce the total number of vehicle miles required to carry out everyday life. Unlike other solutions to congestion such as the lane-expansion of roads, ridesharing does not increase linearly with the number of additional individuals looking to commute and is therefore a much more sustainable form of transportation. Additionally, under the assumption that autonomous vehicles are to be road-ready in the near future, Kornhauser’s research looks at the feasibility of replacing personal transportation with an autonomous taxi (aTaxi) network that has vehicle stations placed in a dense grid layout, allowing individuals to walk to a nearby station and hop in a waiting vehicle. The research that follows takes the national modeling problem out of a theoretical representation of demand to an actual subset of real-life transportation demand by considering the New York City Taxicab and Limousine Commission (TLC) trips taken during 2013. Modeling transportation demand using an actual, albeit smaller, data set instead of a synthetic data set, provides validation for, and a basis of extrapolation to, a national ridesharing model. Using all recorded taxi trips taken in Manhattan and the surrounding boroughs of New York City, this thesis examines current trends and characteristics of the NYC taxicab service. It then investigates how various ridesharing systems, similar to that in Kornhauser’s research, would perform if they were implemented to replace the current network system. Furthermore, the research in this thesis lends itself to the possibility for what would happen if the existing NYC TLC fleet were replaced with autonomous taxis, anticipating that the current progress of autonomous car development will continue.
Extent: 153 pages
URI: http://arks.princeton.edu/ark:/88435/dsp01p2676x91d
Type of Material: Princeton University Senior Theses
Language: en_US
Appears in Collections:Operations Research and Financial Engineering, 2000-2016

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