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https://nap.nationalacademies.org/catalog/27432/critical-issues-in-transportation-for-2024-and-beyond

Societal Impacts of Highly Automated Cars Using the Toolbox of System Dynamics

Rationale: Increasing digitalization and automation is expected to significantly change the transport system, mobility and settlement structures. Till a decade ago automated, self-driving vehicles were nothing more than an unrealistic (childhood) dream in science fiction movies and books. 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. Due to the interconnectedness of mobility and land-use decisions, it is necessary to take a systemic look at societal impacts, particularly effects on traffic volumes, emissions and social exclusion. Background: The authors and their institutions have been and are involved in several national and European research projects analysing potential societal impacts of automated driving through simulations, analysis of user expectations and scenario techniques. First large-scale simulations within the EU-funded project CityMobil (Towards Advanced Road Transport for the Urban Environment, 2006-2011) demonstrated that automated vehicles integrated into public transport have a potential to reduce car mileage travelled and improve carbon footprint even without changes in propulsion technology. On the contrary, privately owned automated vehicles lead to an increase in car mileage travelled and carbon footprint, unless propulsion technology is changed. The findings of the Austrian project Shared Autonomy show potential contributions of automated cars to improve the environmental situation and social inclusion in rural areas. These effects are, however, dependent on future use cases of the new technology and will only materialise if implemented in form of shared mobility rather than privately owned cars. Within this paper, the authors will present results of the nationally funded Austrian project SAFiP (System Scenarios Automated Driving in Personal Mobility) that looks at the national territory of Austria and builds upon those previous projects. SAFiP uses a multi-methodical approach of scenario technics, forecasting and backcasting embedded in a dialogue with experts and stakeholders from politics, administration, science, industry and civil society. Method: The relationship between vehicle automation, travel demand and environmental effects consists of a multitude of complex cause-effect-chains and loops. The toolbox of System Dynamics offers an appropriate method to deal with such complexities. Therefore, the authors update the System Dynamics model MARS (Metropolitan Activity Relocation Simulator) that was already applied to assess scenarios of automated driving in four European cities in the CityMobil project ten years ago. In a first step, Causal Loop Diagrams are used to analyse and discuss relevant cause-effect-relations and their resulting potential impacts for the Austrian case study. The results of this qualitative analysis are used to adapt the existing Stock-Flow-Model of the Austrian land use and transport demand system to automated driving and mobility sharing services. The modified Stock-Flow-Model is then used for a quantitative impact assessment. Sensitivity analyses in form of Monte-Carlo-Simulations are employed to tackle the high level of uncertainty concerning key factors. Findings, results: The level of automation of driving, expressed as the share of highly and fully automated vehicles within the vehicle fleet, is influencing all key factors of mode choice and travel demand - such as travel time, weighted costs of use and the availability of different means of transport - via different cause-effect-chains and feedback loops. The authors 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. E.g. automated and remote parking produces the following cause-effect-chain: Highly automated vehicles can park on their own, reducing or even avoiding the necessity to search for appropriate parking places close to the destination. If there is a higher level of automation then parking place searching time for passengers decreases. This results in lower generalised costs for travel time and hence higher attractiveness and use of private cars, resulting in an increase in car mileage travelled. The identified qualitative cause-effect-chains have been implemented in MARS Austria and sensitivity tests have been carried out. For a 90 percent market share of level 4 and 5 cars in 2050 the widening of the range of users has the highest impact on a national level, potentially increasing car mileage by about 17 percent whereas remote parking increases car mileage by about 5 percent in total, ranging from about 1 percent in peripheral districts to about 17 percent in Vienna.

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  • Supplemental Notes:
    • Abstract used by permission of Association for European Transport.
  • Corporate Authors:

    Association for European Transport (AET)

    1 Vernon Mews, Vernon Street, West Kensington
    London W14 0RL,    
  • Authors:
    • Pfaffenbichler, Paul
    • Gühnemann, Astrid
    • Klementschitz, Roman
    • Emberger, Günter
    • Shepherd, Simon
  • Conference:
  • Publication Date: 2019

Language

  • English

Media Info

  • Media Type: Digital/other
  • Pagination: 11p
  • Monograph Title: European Transport Conference 2019

Subject/Index Terms

Filing Info

  • Accession Number: 01753734
  • Record Type: Publication
  • Files: TRIS
  • Created Date: Sep 29 2020 11:19AM