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Prioritizing Safety or Traffic Flow? Qualitative Study on Highly Automated Vehicles’ Potential to Prevent Pedestrian Crashes with Two Different Ambitions

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  • Roni Utriainen

    (Transport Research Centre Verne, Tampere University, FI-33014 Tampere, Finland)

  • Markus Pöllänen

    (Transport Research Centre Verne, Tampere University, FI-33014 Tampere, Finland)

Abstract

Interaction between drivers and pedestrians enables pedestrians to cross the street without conflicts. When highly automated vehicles (HAVs) become prevalent, interaction will change. Although HAVs manage to identify pedestrians, they may not be able to assess pedestrians’ intentions. This study discusses two different ambitions: Prioritizing pedestrian safety and prioritizing efficient traffic flow; and how these two affect the possibilities to avoid fatal crashes between pedestrians and passenger cars. HAVs’ hypothetical possibilities to avoid different crash scenarios are evaluated based on 40 in-depth investigated fatal pedestrian crashes, which occurred with manually-driven cars in Finland in 2014–2016. When HAVs prioritize pedestrian safety, they decrease speed near pedestrians as a precaution which affects traffic flow due to frequent decelerations. When HAVs prioritize efficient traffic flow, they only decelerate, when pedestrians are in a collision course. The study shows that neither of these approaches can be applied in all traffic environments, and all of the studied crashes would not likely be avoidable with HAVs even when prioritizing pedestrian safety. The high expectations of HAVs’ safety benefits may not be realized, and in addition to safety and traffic flow, there are many other objectives in traffic which need to be considered.

Suggested Citation

  • Roni Utriainen & Markus Pöllänen, 2020. "Prioritizing Safety or Traffic Flow? Qualitative Study on Highly Automated Vehicles’ Potential to Prevent Pedestrian Crashes with Two Different Ambitions," Sustainability, MDPI, vol. 12(8), pages 1-15, April.
  • Handle: RePEc:gam:jsusta:v:12:y:2020:i:8:p:3206-:d:345913
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    References listed on IDEAS

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    1. Dai, Dajun, 2012. "Identifying clusters and risk factors of injuries in pedestrian–vehicle crashes in a GIS environment," Journal of Transport Geography, Elsevier, vol. 24(C), pages 206-214.
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