Self-design mobile agent based approach for road traffic data collection

Today, the collection of high-quality data serves as the basis for precise and complex analysis, facilitating the formulation of answers to relevant questions, improving decision-making processes and evaluating results. In this regard, the importance of data continues to grow in various aspects of our society and daily lives. Despite its importance, data collection poses challenges inherent to the massive quantity of data, the heterogeneity and the sources of distributed data. In recent years, several research projects have addressed this type of problem, relying on the use of advanced tools and modern methods. Our objective is to take advantage of the artificial intelligence technology including the agent software and reinforcement learning to improve the data collection process. In this paper, we focus on data collection in the transportation domain where we present a new approach for road traffic data collection based on self-design mobile agent and reinforcement learning. Then, we choose to show our approach with a case study presenting the collection of road traffic data, as road environment we choose “Bab Saadoun roundabout, Tunis, Tunisia”.