A Geographical Analysis of Optimal Queue Locations for Autonomous Vehicles



Heuwinkel, Jeffrey Raak

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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.



Autonomous vehicles, Population density, Autonomous vehicle queue, Location-allocation, Ridesharing, Smart city