Driving towards Sustainability: A Neural Network-Based Prediction of the Traffic-Related Effects on Road Users in the UAE

Abstract

Transportation is fundamental, granting access to goods, services, and economic opportunities. Ensuring sustainable transportation, especially in vehicular modes, is crucial for the pillars of social, economic, and environmental sustainability. High-traffic countries, like the United Arab Emirates (UAE), grapple with significant challenges to this end. This study delves into the repercussions of traffic-related incidents on UAE road users and their intricate links to the social and economic dimensions of sustainability. To achieve this, this work examines the influential demographic factors contributing to incidents, utilizing artificial neural network models to predict the likelihood of individuals experiencing traffic tickets and accidents. Findings reveal associations between gender, driving frequency, age, nationality, and reported incident frequency. Men experience more accidents and tickets than women. Age exhibits a negative linear relationship with incident occurrence, while driving experience shows a positive linear relationship. Nationalities and cultural backgrounds influence road users’ adherence to traffic rules. The predictive models in this study demonstrate their high accuracy, with 93.7% precision in predicting tickets and 95.8% in predicting accidents. These insights offer valuable information for stakeholders, including government entities, road users, contractors, and designers, contributing to the enhancement of the social and economic aspects of road sustainability.