In SimSAEV, the partners aim to identify environmental and socioeconomic effects of shared autonomous electric vehicles (SAEVs), as wells as synergies and trade-offs between them. For this purpose, an agent-based traffic simulation of Vienna is extended with a module that allows the inclusion of SAEVs in the simulation of scenarios. The project supports the formation of transport policies that lead to a reduction in CO2 emissions, while maintaining an efficient and inclusive transport system.

The agent-based MATSim model is used to represent the complex interactions between the transportation system, socioeconomic factors and environmental factors. Several scenarios that differ in their assumption concerning SAEV technology, SAEV market structure, consumer preferences, and policy interventions are simulated. The scenarios are assessed in regards of traffic indicators but also global and local emissions.

One main result is the publication in which the setup of the MATSim simulation for Vienna is explained. A first glimpse into the scenario outputs was presented at the 100th Annual Meeting of the TRB. In two further publications, different scenario settings are explained and evaluated. One setup is the introduction of SAEVs in the inner city of Vienna. The other setup is introducing SAEVs as a feeder to the public transport system in the periphery of the city. While one publication focuses on the socio-demographic analysis of the users of the service, the other journal paper analyses the changes of the exposure to emissions. The finding include that despite large fleets and low costs, SAEVs have limited impact on reducing car use, with each vehicle replacing only 2–4 private cars due to minimal ride-sharing. The study on the exposure to fine particles reveals that vulnerable groups — especially youth and inner-city residents — are most affected during peak hours. While SAEVs help lower emissions, the resulting air quality improvements are unequally distributed, benefiting car users the least exposed but most responsible for pollution.