Impact of Different Penetration Rates of Shared Autonomous Vehicles on Traffic: Case Study of Budapest

Abstract

The accelerating emergence of vehicle automation and the anticipation of the advent of shared mobility through fully autonomous vehicles indicate the beginning of a new era of mobility which has the potential to reshape the future of transport in urban areas. In light of such developments, it is important that communities prepare to adapt to the changes they might entail. Therefore, in this paper, traffic flow theory, simulation-based dynamic traffic assignment, and a computer experiment using PTV Visum software were employed to study the impact of different market penetration rates of shared autonomous vehicles (SAVs) on a city-size traffic system. The city of Budapest during morning peak period was chosen as a case study, and a simulation model was created by incorporating SAV elements and their interrelationships into the existing traffic model of the case study city; three alternative future penetration rates were examined in relation to five key performance indicators (KPIs). The simulation results indicated that the implementation of the SAV system has a positive effect on traffic performance. Based on the relationships between the modeled SAV demand shares and the network’s KPIs in the designed scenarios, the overall network performance showed improvement along with an increase in the SAV demand share.

Enhancing Sustainable Mobility: Evaluating New Bicycle and Pedestrian Links to Car-Oriented Industrial Parks with ARAS-G MCDM Approach

“…where O n is the number of commuters passing daily through the network segment n ∈ N, P is the population in the town or adjacent territories from where the workers will arrive, E is the employment-to-population ratio, P a is the number of people who can access the job places at a fixed distance calculated by Equation (20), k c is the travel mode coefficient for the cycling travel mode (desired or measured), D is the total number of jobs in the destination area.…”

“…This situation can be attributed to traditional transport planning methods that calculate traffic flows between transportation districts without considering local street segment-level changes, but traditional methods are not sensible to micro-level or pathway segment-level changes. The present generation of traffic planners often relies on macro-level data, despite advancements in processing speed and data capacity [19][20][21][22]. Micro-simulation, typically performed with software such as PTV VISIM, analyses changes in existing traffic flows, link delays, volumes, and traffic light phases [23,24], but often fails to provide detailed predictions at the neighborhood level.…”

The Interrelationship between Road Pricing Acceptability and Self-Driving Vehicle Adoption: Insights from Four Countries

“…A study that accommodated ride-sharing options for a fully electric SAV fleet, as the adoption of electric vehicles is growing worldwide [84], investigated the implications of SAVs on the performance of the Budapest road network using simulation-based dynamic traffic assignment. The results showed that increasing the SAVs share would improve the overall network performance [85]. An analysis of the potential benefits of dynamic ride sharing using SAVs over traditional taxis in New York City demonstrated that the fleet size could be reduced by 59% without increasing the waiting time.…”

Examining the Impact of Hysteresis on the Projected Adoption of Autonomous Vehicles

“…Some researchers tend to create several scenarios to cover the possible cases of integrating AVs into road networks as this new technology is not yet entirely developed (see e.g. [10][11][12][13]), which consumes time and effort.…”