Parking Strategies and Outcomes for Shared Autonomous Vehicle Fleet Operations
Publication: Journal of Transportation Engineering, Part A: Systems
Volume 150, Issue 4
Abstract
Parking spots are a premium commodity, especially in dense downtown settings, so this study examines the service impacts of shared autonomous vehicles (SAVs) parking in legal on- or off-street locations when idle across Travis County in Austin, Texas. Using an agent-based activity-based travel demand model with dynamic traffic simulation, two restricted-parking strategies for SAVs were simulated. SAVs either found the nearest available parking spot or the lowest-cost spot (via a tradeoff of parking fees and distance-based costs). Two comparisons were conducted to analyze the impacts of these strategies. First, two restricted parking strategies were compared, where SAVs park without competition with private human-driven vehicles (HVs) for parking locations. Second, a more realistic analysis compared two SAV parking strategies with a scenario where SAVs remain idle in place. Private HVs in all scenarios and strategies of this comparison park at the closest designated location unless they opt for private parking. Using a supply of 8,400 aggregated parking locations in Austin, this study simulated fleet performance under different trip demands, with SAV fares of $0.62 per kilometer ($1 per mile) plus a $1 fixed pickup fee with dynamic ridesharing permitted. Parking costs were negligible in both SAV parking search strategies applied to the Austin network because of the region’s provision of mostly free parking. Requiring SAVs to park on designated on- and off-street parking locations and parking lots (restricted parking) also increased parking costs for HV drivers by up to 22% since SAVs occupied some free parking spaces, especially in the least-cost parking search strategy.
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Data Availability Statement
Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
The work done in this paper was sponsored by the US DOE Vehicle Technologies Office (VTO) under the Systems and Modeling for Accelerated Research in Transportation (SMART) Mobility Laboratory Consortium, an initiative of the Energy Efficient Mobility Systems (EEMS) Program. The US Government retains for itself, and others acting on its behalf, a paid-up nonexclusive, irrevocable worldwide license in said article to reproduce, prepare derivative works, distribute copies to the public, and perform publicly and display publicly, by or on behalf of the Government. The study was also funded by Ford Motor Company in Project No. 001272-URP0113. The last author was supported by the National Science Foundation Graduate Research Fellowship Program under Grant No. DGE-1610403. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author and do not necessarily reflect the views of the National Science Foundation. The authors also acknowledge Jade (Maizy) Jeong for her excellent editing and submission support.
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Published In
Journal of Transportation Engineering, Part A: Systems
Volume 150 • Issue 4 • April 2024
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© 2024 American Society of Civil Engineers.
History
Received: Feb 21, 2023
Accepted: Nov 14, 2023
Published online: Jan 31, 2024
Published in print: Apr 1, 2024
Discussion open until: Jun 30, 2024
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