A Systemic Analysis of Impacts of Individual and Shared Automated Mobility in Austria

Rationale: Increasing digitalization and automation is expected to significantly change the transport system, mobility and settlement structures. A decade ago automated, self-driving vehicles were nothing more than an unrealistic (boyhood) dream. But today the concept of highly and fully automated vehicles is rapidly becoming a reality, with a series of real-world trial applications underway. Government plans and industry predictions expect automation to be introduced from the early 2020s onwards. Nevertheless, there is still a high level of uncertainty in which form and to what extent automated vehicles will enter the market. Furthermore, there are ongoing discussions concerning net effects of positive and negative aspects of automation.

Background: The authors have been involved in several research projects analyzing potential impacts of automated driving. The EU funded project CityMobil (Towards Advanced Road Transport for the Urban Environment) was one of first to address automated driving on a large scale. As part of this project the System Dynamics based model MARS (Metropolitan Activity Relocation Simulator) was adapted to assess scenarios of automated driving in four European cities. Simulations demonstrated that automated vehicles integrated into public transport have a potential to reduce car kilometers travelled and improve carbon footprint. On the contrary, privately owned automated vehicles lead to an increase in car kilometers travelled and carbon footprint, unless propulsion technology is changed.

While the focus of CityMobil was on the urban scale, the nationally funded Austrian project Shared Autonomy (Potential Effects of the Take-up of Automated Vehicles in Rural Areas – own translation) focused on rural areas. The findings of Shared Autonomy show potential contributions of automated cars to improve the environmental situation and social inclusion in rural areas.

Finally, the nationally funded Austrian project SAFiP (System Scenarios Automated Driving in Personal Mobility) takes a look at the national territory of Austria.

Method: The relationship between vehicle automation, travel demand and environmental effects consists of a multitude of complex cause-effect-chains. The toolbox of System Dynamics offers appropriate methods to tackle such complexities. Causal Loop Diagrams are used to analyze and discuss relevant cause-effect-chains and are used to adapt an existing Stock-Flow-Model of the Austrian land use and transport demand system. The modified Stock-Flow-Model is used for a quantitative impact assessment. Sensitivity analysis in form of Monte-Carlo-Simulations is employed to tackle the high level of uncertainty concerning key factors.

Findings, results: The key factors, influencing mode choice and travel demand, are generalized costs of travel time, weighted costs of use and availability. The automation of driving, expressed as the share of highly and fully automated vehicles in the fleet, is influencing all three key factors via different cause-effect-chains and feedback loops. In SAFiP we identified four key impact sources: automated and remote parking, road capacity and travel speed, value of in-vehicle time and widening the range of users. Sensitivity tests for each of the impact sources have been carried out. Widening the range of users has the highest impact on a national level, potentially increasing car kilometers by about 17 percent in 2050. Remote parking increases car kilometers by about 5 percent in total, ranging from about 1 percent in peripheral districts to about 17 percent in Vienna.