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Exploring the Impact of Shared Autonomous Vehicles on Urban Parking Demand: An Agent-Based Simulation Approach
Although recent studies of Shared Autonomous Vehicles (SAVs) have explored the economic costs and environmental impacts of this technology, little is known about how SAVs can change urban forms, especially by reducing the demand for parking. This study estimates the potential impact of SAV system on urban parking demand under different system operation scenarios with the help of an agent-based simulation model. The simulation results indicate that we may be able to eliminate up to 90% of parking demand for clients who adopt the system, at a low market penetration rate of 2%. The results also suggest that different SAV operation strategies and client's preferences may lead to different spatial distribution of urban parking demand.
Key findings
With no ride-sharing happening in the fleet, parking demand is sensitive to the number of AVs on the road.
There has to be enough vehicles on the road to provide quality service and keep the wait time at 2 minutes.
A ride-sharing model is sensitive to parking demand based on how many people are willing to share.
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