Autonomous Vehicles and their Impact on Parking Search

Abstract

Parking is a major constraint for car users and therefore an important factor in mode choice decisions. In this paper we introduce a model to simulate parking search behavior for cars within a multi-agent transport simulation, including full simulation of all steps of parking search, such as walking to and from the vehicle. This is combined with the capabilities of privately owned autonomous vehicles (AVs), which may park automatically, often in other locations than conventional cars, once they are not in use. Three different strategies for AVs to park are developed: (1) Conventional parking search, (2) parking at a designated AV lot, and (3) empty cruising, where vehicles do not use any parking space, but keep on driving. We apply the simulation model to a residential neighborhood in central Berlin, where parking pressure is generally high and apply different shares of AV usage to the synthetic population used. This allows a detailed evaluation of effects for both AV and conventional vehicle owners. Results suggest that the usage of designated parking lots may be the most beneficial solution for most users, with both vehicle wait times and parking search durations being the lowest.

Sustainable Renewal Methods of Urban Public Parking Spaces under the Scenario of Shared Autonomous Vehicles (SAV): A Review and a Proposal

“…Recent research by Zhang and Wang [59] analyzed the demand reduction trajectory during the transition period when there is a mix of SAVs, privately owned AVs (PAVs), shared conventional vehicles (SCVs), and conventional private vehicles (CPVs). Bischoff et al [60] introduced multi-agent technology to simulate the parking search behavior of autonomous vehicles under three different parking strategies (regular parking, parking in designated lots, and empty cruising), and applied the model to a residential area in downtown Berlin for detailed evaluation. Simons et al [61] investigated the sustainability of downtown parking when autonomous vehicles are present.…”

An Occupancy Simulator for a Smart Parking System: Developmental Design and Experimental Considerations

“…This model parameter can be dynamically adjusted. Driving behavior and parking search are complex processes [37][38][39] and several studies have applied realistic behaviors [3,13,19,27], even considering recent trends like driver-less vehicles [40] or specific contexts like a city center [41] and university campuses with specific policies and notable parking supply shortages [42]. We chose two simple parking search behaviors for our model because obtaining an approximate parking occupancy, rather than the most realistic one, was enough for our simulator goal.…”

Simulation-Based Assessment of Parking Constraints for Automated Mobility on Demand: A Case Study of Zurich

“…In an agent-based simulation, Bischoff et al [14] investigate the negative impact of excessive cruising in a neighborhood of Berlin with 4000 parking spaces and a total of 37,000 people traveling. Overall, an average duration of 8:50 [min] is recorded to find an empty parking space.…”