A Case Study in Budapest, Hungary Showed Scenarios with Substantial Shared Automated Vehicle Use May Reduce Parking Demand by Up to 83 Percent.

Scenario-Based Mathematical Models Developed for Shared Automated Vehicles in Hungary Revealed Reduced Parking Demand and Environmental Benefits. 

Date Posted
02/29/2024
Identifier
2024-B01832
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Autonomous Vehicle Use and Urban Space Transformation: A Scenario Building and Analyzing Method

Summary Information

Autonomous Vehicles (AVs) offer a great potential to transform road user behavior and help manage urban space more effectively, through shared use options and driverless trips. Evaluating the quantitative and qualitative impacts of AVs requires a well-thought-out scenario building method. This study took into consideration the fleet size, modal share, car ownership, parking preferences, and urban space repurposing in building scenarios for a case study set in Budapest, Hungary. Five scenarios describing the current, transitional, and future terms, combining different types of car ownership and fleet size, were developed to analyze whether AV acceptability and adoption of shared vehicle use could significantly reduce parking demand and, consequently, lead to the repurposing of urban space. The scenarios were created based on a literature review, publicly available data, as well as a user survey that received 150 responses from citizens and relevant actors from the transport field, such as operators and experts. 

METHODOLOGY

In this study, the researchers conducted a survey to gain insights into the aspects needed for scenario building; namely, status quo and change in habits and preferences regarding car ownership, acceptability of AVs and shared use, parking, as well as to determine the priorities in urban space repurposing. To apply the scenarios built and evaluate the impacts of changes in urban space transformation, a mathematical calculation method previously developed by the researchers of this study was used. Specifically, the following five scenarios were developed in this study:

  1. CURRENT: Fleet of conventional vehicles with 21 percent private, 44 percent Private Shared fully Automated Vehicle (PSAV), 13 percent Shared fully Automated Vehicle with Single Occupancy (SAVSO), and 22 percent Shared fully Automated Vehicle with Multiple Occupancies (SAVMO).
  2. NEAR FUTURE: Fleet of conventional vehicles with 43 percent private, 31 percent private shared, seven percent SAVSO, and 19 percent SAVMO.
  3. TRANSITIONAL: Fleet of conventional vehicles with 48 percent (private and/or shared), and a fleet of AVs with ten percent private, seven percent PSAV, 16 percent SAVSO, and 19 percent SAVMO.
  4. FUTURE WITH AVs: Fleet of conventional vehicles with 12 percent (private and/or PSAV, and a fleet of AVs with ten percent private, seven percent PSAV, 52 percent SAVSO, and 19 percent SAVMO.
  5. MIX OF SAV FLEETS: Fleet of AVs with ten percent private, 50 percent SAVSO, and 40 percent SAVMO.

FINDINGS

  • Scenarios 3, 4, and 5 demonstrated substantial reductions in parking space needs by 33 percent (818 spaces), 65 percent (1,618 spaces), and 83 percent (2,072 spaces) respectively, highlighting efficiency improvements.
  • Increasing Shared Autonomous Vehicle (SAV) fleets can lead to pollutant reductions ranging from two to 18 percent in Scenario 3 to five to 45 percent in Scenario 5, showing environmental benefits.
  • Transforming saved parking spaces into green areas could mitigate air pollution, with Scenario 5 indicating potential annual reductions of up to 13 kg of SO2 and CO, 19.4 kg of NOx, and 188 kg of PM10.
Results Type
Deployment Locations