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Applying Machine Learning to Explore Feelings about Sharing the Road with Autonomous Vehicles as a Bicyclist or as a Pedestrian

Author

Listed:
  • Zohreh Asadi-Shekari

    (Centre for Innovative Planning and Development, CIPD, Universiti Teknologi Malaysia, Skudai, Johor Bahru 81310, Johor, Malaysia)

  • Ismaïl Saadi

    (Local Environment & Management Analysis (LEMA), Urban and Environmental Engineering (UEE), University of Liège, Allée de la Découverte 9, Quartier Polytech 1, 4000 Liege, Belgium
    F.R.S.-FNRS, Rue d’Egmont 5, 1050 Brussels, Belgium
    IFSTTAR, COSYS-GRETTIA, University Gustave Eiffel, F-77454 Marne-la-Vallée, France)

  • Mario Cools

    (Local Environment & Management Analysis (LEMA), Urban and Environmental Engineering (UEE), University of Liège, Allée de la Découverte 9, Quartier Polytech 1, 4000 Liege, Belgium
    Department of Informatics, Simulation and Modeling, KU Leuven Campus Brussels, Warmoesberg 26, 1000 Brussels, Belgium
    Faculty of Business Economics, Hasselt University, Agoralaan Gebouw D, 3590 Diepenbeek, Belgium)

Abstract

The current literature on public perceptions of autonomous vehicles focuses on potential users and the target market. However, autonomous vehicles need to operate in a mixed traffic condition, and it is essential to consider the perceptions of road users, especially vulnerable road users. This paper builds explicitly on the limitations of previous studies that did not include a wide range of road users, especially vulnerable road users who often receive less priority. Therefore, this paper considers the perceptions of vulnerable road users towards sharing roads with autonomous vehicles. The data were collected from 795 people. Extreme gradient boosting (XGBoost) and random forests are used to select the most influential independent variables. Then, a decision tree-based model is used to explore the effects of the selected most effective variables on the respondents who approve the use of public streets as a proving ground for autonomous vehicles. The results show that the effect of autonomous vehicles on traffic injuries and fatalities, being safe to share the road with autonomous vehicles, the Elaine Herzberg accident and its outcome, and maximum speed when operating in autonomous are the most influential variables. The results can be used by authorities, companies, policymakers, planners, and other stakeholders.

Suggested Citation

  • Zohreh Asadi-Shekari & Ismaïl Saadi & Mario Cools, 2022. "Applying Machine Learning to Explore Feelings about Sharing the Road with Autonomous Vehicles as a Bicyclist or as a Pedestrian," Sustainability, MDPI, vol. 14(3), pages 1-10, February.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:3:p:1898-:d:743814
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    References listed on IDEAS

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    1. Penmetsa, Praveena & Adanu, Emmanuel Kofi & Wood, Dustin & Wang, Teng & Jones, Steven L., 2019. "Perceptions and expectations of autonomous vehicles – A snapshot of vulnerable road user opinion," Technological Forecasting and Social Change, Elsevier, vol. 143(C), pages 9-13.
    2. Christina Pakusch & Gunnar Stevens & Alexander Boden & Paul Bossauer, 2018. "Unintended Effects of Autonomous Driving: A Study on Mobility Preferences in the Future," Sustainability, MDPI, vol. 10(7), pages 1-22, July.
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    4. Aghaabbasi, Mahdi & Shekari, Zohreh Asadi & Shah, Muhammad Zaly & Olakunle, Oloruntobi & Armaghani, Danial Jahed & Moeinaddini, Mehdi, 2020. "Predicting the use frequency of ride-sourcing by off-campus university students through random forest and Bayesian network techniques," Transportation Research Part A: Policy and Practice, Elsevier, vol. 136(C), pages 262-281.
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    Cited by:

    1. Chengyuan Mao & Wenjiao Xu & Yiwen Huang & Xintong Zhang & Nan Zheng & Xinhuan Zhang, 2023. "Investigation of Passengers’ Perceived Transfer Distance in Urban Rail Transit Stations Using XGBoost and SHAP," Sustainability, MDPI, vol. 15(10), pages 1-22, May.

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