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

Zohreh Asadi-Shekari, Ismaïl Saadi and Mario Cools
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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
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

Sustainability, 2022, vol. 14, issue 3, 1-10

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.

Keywords: autonomous vehicles; vulnerable road users; public perception; machine learning; most effective variables (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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