The foundation of efficient and safe urban transportation systems is intricately designed through the principles of urban planning. Transportation planning stands as a pivotal concern for urban regions worldwide, reflecting the growing need to address mobility, sustainability, and infrastructure challenges in densely populated areas. To address the challenges related to the creation of safe and efficient traffic monitoring system, this research focuses on developing a novel digital-twin-based approach for micro traffic simulation in support of data-driven decision-making. Leveraging the traffic data, obtained through the monitoring one of the busiest intersections in Sofia city, this research workflow shows effective integration of LiDAR data and digital twin concept in intelligent transportation systems (ITS). The research tackles problems related to object classification, trajectory analysis, and reclassification of unrecognised objects by carefully processing of the raw dataset, provided in a heavy .osef format, thereby rendering it to make it suitable for  simulation. The project’s solution for analysis of urban traffic is demonstrated by the usage of SUMO for performing simulations and a Random Forest model for object reclassification. Through the effective demonstration of traffic simulation based on real-time data, the research offers insights into traffic dynamics and possible improvements to urban transportation planning. The proposed workflow’s architecture is possible to be applied in other similar urban settings, providing a scalable solution for both traffic control and urban planning. The results of the study support the wider use of digital twin principles in ITS by highlighting the value of advanced modelling tools and high-quality data in addressing today’s urban transportation challenges.