Exploring the Economic, Environmental, and Travel Implications of Changes in Parking Choices due to Driverless Vehicles: An Agent-Based Simulation Approach

Authored by Corey D Harper, Chris T Hendrickson, Constantine Samaras

Date Published: 2018

DOI: 10.1061/(asce)up.1943-5444.0000488

Sponsors: No sponsors listed

Platforms: No platforms listed

Model Documentation: Other Narrative

Model Code URLs: Model code not found

Abstract

Fully driverless automated vehicles (AVs) could considerably alter the proximity value of parking, due to an AV's ability to drop passengers off at their destination, search for cheaper parking, and return to pick up their occupants when needed. This study estimates the potential impact of privately owned driverless vehicles on vehicle kilometers traveled (VKT), energy use, emissions, and parking revenues in the city of Seattle, Washington, from changes in parking decisions using an agent-based simulation model. Each AV is assumed to consider the cost to drive to each parking spot, the associated daily parking cost, and the parking availability at each location, and the AV ranks each choice in terms of economic cost. The simulation results indicate that at low penetration rates (5-25\% AV penetration), AVs in downtown Seattle would travel an additional 5.6-6.4 km/day (3.5-4.0 mi/day) on average, and that, at high penetration rates (50-100\% AV penetration), AVs would travel an additional 9.0-13.5 km/day (5.6-8.4 mi/day) on average. The results also suggest that as AV penetration rates increase, parking lot revenues decrease significantly and could likely decline to the point where operating a lot is unsustainable economically, if no parking-demand management policies are implemented. This could lead to changes in land use as the amount of parking needed in urban areas is reduced and cars move away from the downtown area for cheaper parking. This analysis provides an illustration of the first-order effects of AVs on the built environment and could help inform near- and long-term policy and infrastructure decisions during the transition to automation. (C) 2018 American Society of Civil Engineers.

Tags

Agent-based model Parking Cities Autonomous vehicles Driverless automated vehicles