Simulation Models Used to Forecast Changes in Parking and Traffic Flow If a Shared Autonomous Vehicle Fleet Replaced Private Vehicles, Using Okinawa, Japan as a Case Study.
Okinawa, Japan
Impacts of shared autonomous vehicles: Tradeoff between parking demand reduction and congestion increase
Summary Information
Shared Autonomous Vehicles (SAVs) have the potential to make significant impacts on transportation patterns and traveler behavior as they become a part of existing networks. In particular, the use of SAVs is expected to reduce parking demand, while at the same time increasing traffic congestion due to increases in empty SAVs traveling to pick up passengers. Researchers at the University of Tokyo sought to analyze the localized impact of SAV deployment using simulation modeling methods.
Methodology
Researchers used simulation of urban road networks with dynamic route choice (SOUND) models to simulate driver behavior, and used Okinawa, Japan, as a case study, focusing on zones in Naha City and the surrounding area. Two simulations were run: one representing contemporary conditions, and another introducing SAVs and reducing the overall fleet size to represent a shift towards shared automated vehicles.
Findings
- As part of the preliminary activities for the experiment, the researchers determined that a fleet of 60,000 SAVs, less than 6 percent as large as the existing number of registered private vehicles, was sufficient to meet travelers' needs to an equivalent level of service in terms of average wait time.
- The SAV simulation showed an overall 94 percent reduction in parking demand compared to the simulation without SAVs. This reduction was proportionally greatest in zones that were dominated by office buildings.
- Overall congestion increased in the SAV simulation, with total delay time increasing by 32.5 percent and average travel speed decreasing by 9.2 percent compared to the non-SAV scenario.
