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

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.

Autonomous Vehicle Technology and Technology Acceptance: The Role of Technological Readiness on Consumers' Attitudes Towards Driverless Cars and Intention to Use in the Future

“…Tan et al (2019) [59] investigated choice behavior based on logistics models, considering participants' personal characteristics, travel demand, and cognitive aspects of autonomous vehicles. Another study by Asadi-Shekari et al (2022) [60] applied machine learning to explore emotions about sharing the road with autonomous vehicles as a cyclist or pedestrian. Azevedo et al (2016) [61] conducted a study focusing on the microsimulation of the supply and demand of autonomous mobility on demand.…”

Investigation of Passengers’ Perceived Transfer Distance in Urban Rail Transit Stations Using XGBoost and SHAP

“…XGBoost has been widely used in various areas of transportation, such as traveler perception, traffic volume prediction, transportation safety, and risk analysis, and has achieved better results than traditional algorithms. Asadi-Shekari [36] introduced an algorithm based on XGBoost and analyzed the perceptions of vulnerable road users towards sharing the road with autonomous vehicles. The results showed that XGBoost is highly effective and accurate in predicting the views of cyclists or pedestrians.…”