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Highway infrastructure and safety implications of AV technology in the motor carrier industry

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  • Bernard Bracy, Jill M.
  • Bao, Ken Q.
  • Mundy, Ray A.

Abstract

This study explores the infrastructure investments needed to support the adoption of autonomous vehicles and provides potential safety benefits from AV technologies as a possible motivation for such investments. We assume that the motor carrier industry will be (one of) the first adopter(s) of such technologies, and therefore is the focus of this study. Using large truck crash data from 2013 through 2015 obtained from the Missouri State Highway Patrol, Chi-square Automatic Interaction Detection decision trees are estimated to examine the effect of AV technologies on motor carrier crash severity. Results suggest that the greatest contributory predictors of crash severity outcomes are driving too fast for conditions, distracted/inattentive driving, overcorrecting and driving under the influence of alcohol. If these circumstances are altered by AV technologies, it is suggested that between 117 and 193 severe crashes involving large trucks could be prevented annually in Missouri alone. To render such safety benefits, key vehicle needs include autonomously controlling acceleration and steering, monitoring of the environment, and responding to dynamic driving environments without the need for human intervention. Importantly, the safe operations of a system that can perform such AV tasks require readable lane markings to capitalize on potential safety benefits.

Suggested Citation

  • Bernard Bracy, Jill M. & Bao, Ken Q. & Mundy, Ray A., 2019. "Highway infrastructure and safety implications of AV technology in the motor carrier industry," Research in Transportation Economics, Elsevier, vol. 77(C).
    • Handle: RePEc:eee:retrec:v:77:y:2019:i:c:s0739885919302707
    DOI: 10.1016/j.retrec.2019.100758
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    References listed on IDEAS

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    1. Kishore Bhoopalam, A. & Agatz, N.A.H. & Zuidwijk, R.A., 2017. "Planning of Truck Platoons: a Literature Review and Directions for Future Research," ERIM Report Series Research in Management ERS-2017-010-LIS, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
    2. G. V. Kass, 1980. "An Exploratory Technique for Investigating Large Quantities of Categorical Data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 29(2), pages 119-127, June.
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    Cited by:

    1. Alonso Raposo, María & Grosso, Monica & Mourtzouchou, Andromachi & Krause, Jette & Duboz, Amandine & Ciuffo, Biagio, 2022. "Economic implications of a connected and automated mobility in Europe," Research in Transportation Economics, Elsevier, vol. 92(C).

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    More about this item

    Keywords

    Autonomous vehicles; Connected vehicles; AV; CV; Highway safety; CHAID; Infrastructure; Highway infrastructure; Large truck crashes;
    All these keywords.

    JEL classification:

    • R41 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Transportation Economics - - - Transportation: Demand, Supply, and Congestion; Travel Time; Safety and Accidents; Transportation Noise
    • R58 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Regional Government Analysis - - - Regional Development Planning and Policy
    • R40 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Transportation Economics - - - General
    • R49 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Transportation Economics - - - Other
    • R42 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Transportation Economics - - - Government and Private Investment Analysis; Road Maintenance; Transportation Planning
    • R48 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Transportation Economics - - - Government Pricing and Policy

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