Autonomous Vehicles Adoption Classification for Future Mobility in UAE Using Machine Learning

Abstract

Autonomous vehicles will fundamentally change the future mobility of people and goods. However, there are many factors that raise the concerns of the residents of the United Arab Emirates towards adopting autonomous vehicles. The distrust of the people is based on factors such as safety, trust, privacy, accessibility, ethics, and other inherent concerns that are difficult to quantify and model. In this paper, we have captured data using an online survey designed for residents of the UAE in order to understand their concerns and the corresponding factors. We address the adoption of AV in the UAE as a classification problem, in which machine learning algorithms are applied to classify the participants of the survey into categories/classes representing their willingness to purchase an AV. The applied machine learning techniques include: Ada-boost, KNN, Neural Networks, SVM, Decision Trees, Random Forest, Naïve Bayes, and Gradient Boosting. We provide a comparison between the applied machine learning classifiers to come up with the best classifier model, which will represent the prediction model for the adoption of future mobility technologies for the UAE public. Our results demonstrate the potential of machine learning to accurately forecast AV adoption in the UAE based on user data.