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A Geographical Analysis of Optimal Queue Locations for Autonomous Vehicles

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dc.contributor.advisor Rice, Matthew Heuwinkel, Jeffrey Raak
dc.creator Heuwinkel, Jeffrey Raak 2020-04-10 2020-06-23T17:49:41Z 2020-06-23T17:49:41Z
dc.description.abstract As autonomous vehicles (AVs) get introduced in urban areas, the problem of effectively and efficiently serving the population needs to be addressed. This thesis explores AV introduction into cities, population density and demand dynamics, queueing strategies, and a streetscape analysis to analyze needed modifications to the streetscape. This research study focuses on the geographic region of Washington D.C. with a temporal scope of morning rush hour. Current ridesharing methods have been shown to cause traffic congestion in densely populated urban areas where demand would be high. The coordinated introduction of AV service in urban areas, including planned queue locations, may replace current ridesharing services. The planned AV queues would facilitate efficient entrance and egress from vehicles using existing curbside locations, with the AV queue replacing AVs as needed through modeled population demand. To determine the optimal AV queue locations, high demand areas are identified through an analysis of population density, and specific locations for AV queues are determined through location-allocation analysis using barriers, demand points, and distance thresholds from previous research. Finally, the current and potential future streetscapes are analyzed with examples presented. en_US
dc.language.iso en en_US
dc.subject autonomous vehicles en_US
dc.subject population density en_US
dc.subject autonomous vehicle queue en_US
dc.subject location-allocation en_US
dc.subject ridesharing en_US
dc.subject smart city en_US
dc.title A Geographical Analysis of Optimal Queue Locations for Autonomous Vehicles en_US
dc.type Thesis en_US Master of Science in Geographic and Cartographic Sciences en_US Master's en_US Geographic and Cartographic Sciences en_US George Mason University en_US

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