  Parking is a crucial element of the driving experience in urban
transportation systems. Especially in the coming era of Shared Autonomous
Vehicles (SAVs), parking operations in urban transportation networks will
inevitably change. Parking stations will serve as storage places for unused
vehicles and depots that control the level-of-service of SAVs. This study
presents an Analytical Parking Planning Model (APPM) for the SAV environment to
provide broader insights into parking planning decisions. Two specific planning
scenarios are considered for the APPM: (i) Single-zone APPM (S-APPM), which
considers the target area as a single homogeneous zone, and (ii) Two-zone APPM
(T-APPM), which considers the target area as two different zones, such as city
center and suburban area. S-APPM offers a closed-form solution to find the
optimal density of parking stations and parking spaces and the optimal number
of SAV fleets, which is beneficial for understanding the explicit relationship
between planning decisions and the given environments, including demand density
and cost factors. In addition, to incorporate different macroscopic
characteristics across two zones, T-APPM accounts for inter- and intra-zonal
passenger trips and the relocation of vehicles. We conduct a case study to
demonstrate the proposed method with the actual data collected in Seoul
Metropolitan Area, South Korea. Sensitivity analyses with respect to cost
factors are performed to provide decision-makers with further insights. Also,
we find that the optimal densities of parking stations and spaces in the target
area are much lower than the current situations.
