Policy and society related implications of automated driving: A review of literature and directions for future research

By Dimitris Milakis, B. van Arem, Bert van Wee. Journal of Intelligent Transportation Systems / Taylor & Francis, 2017

868 citations recorded by the source corpus.

ABSTRACT In this paper, the potential effects of automated driving that are relevant to policy and society are explored, findings discussed in literature about those effects are reviewed and areas for future research are identified. The structure of our review is based on the ripple effect concept, which represents the implications of automated vehicles at three different stages: first-order (traffic, travel cost, and travel choices), second-order (vehicle ownership and sharing, location choices and land use, and transport infrastructure), and third-order (energy consumption, air pollution, safety, social equity, economy, and public health). Our review shows that first-order impacts on road capacity, fuel efficiency, emissions, and accidents risk are expected to be beneficial. The magnitude of these benefits will likely increase with the level of automation and cooperation and with the penetration rate of these systems. The synergistic effects between vehicle automation, sharing, and electrification can multiply these benefits. However, studies confirm that automated vehicles can induce additional travel demand because of more and longer vehicle trips. Potential land use changes have not been included in these estimations about excessive travel demand. Other third-order benefits on safety, economy, public health and social equity still remain unclear. Therefore, the balance between the short-term benefits and long-term impacts of vehicle automation remains an open question.

Subfields: Automotive Engineering, Transportation

View Paper: https://www.semanticscholar.org/paper/52d630cf8c54fade12b853f5ad1adf5917f196d0

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Citations in the corpus

Game Theoretic Analysis of Road User Safety Scenarios Involving Autonomous Vehicles (2018, background): “the literature to shed light on these aspects: game theoretical and/or statistical approaches have been advanced. Surveys and predictions of the future transport systems can be found, for example, in [5] and [6]. The latter also gained mass attention thanks to popular divulgative versions such as [7”
Assessing the effect of autonomous driving on value of travel time savings: A comparison between current and future preferences (2019, background): “…on the street increases (Hartmann et al., 2017, Calvert et al., 2017) and especially if connectivity between the AVs (and infrastructure) is given (Milakis et al., 2017), policy measures are needed to ensure an efficient use of the technology from the point of its implementation into the…”
Development and transport implications of automated vehicles in the Netherlands: scenarios for 2030 and 2050 (2017, background): “Earlier travel demand studies have shown that total VKT could increase between 4% and 26% after introduction of automated vehicles (Milakis et al., 2016), • the Dutch government is expected to take measures (e.g. travel demand management) to curb growth of travel and subsequent externalities in…”
Governing autonomous vehicles: emerging responses for safety, liability, privacy, cybersecurity, and industry risks (2018, background): “…as to levels of congestion, the environment, land use, public infrastructure investment, government revenues or even organ shortage (Brodsky, 2016; Milakis et al., 2017; Bischo ﬀ & Maciejewski, 2016; Clem-ents & Kockelman, 2017), these risks have not received enough attention from governments…”
Impacts of automated vehicles on travel behaviour and land use: an international review of modelling studies (2018, background): “As AVs are not yet available to the public at large, it is impossible to gather empirical data for further analysis and studies in the literature mainly apply various models and simulations to analyse the impacts of AVs (Milakis et al., 2017; Zhang, 2017).”
A multi-level model on automated vehicle acceptance (MAVA): a review-based study (2019, result): “Similar to Milakis, Van Arem, and Van Wee (2017) in their ripple effect of automated driving model, the moral-normative perception of risks and benefits in our model represents higher-order implications of AVs on energy consumption, air pollution, economy, public health and social equity.”
Tool Learning with Foundation Models (2023, neutral): “Therefore, before we apply these models to high-stake scenarios such as autonomous driving (Milakis et al., 2017) and clinical trials (Matheny et al., 2019), it is essential to thoroughly discuss to what extent should we allow AI to engage in human lives.”