The process of ensuring efficient and safe urban transportation is closely linked to urban planning, particularly through the aspects of transportation planning. Transportation planning is a pivotal concern for urban regions worldwide, reflecting the growing need to increase mobility while ensuring safety and sustainability in densely populated areas. This research focuses on developing a novel digital-twin-based approach for micro-traffic simulation to support data-driven decision-making for increasing traffic safety through scenario planning. Leveraging the traffic data obtained through monitoring one of the busiest intersections in Sofia city, this research workflow shows the effective integration of LiDAR data and the urban digital twin concept in intelligent transportation systems (ITS). The research addresses problems related to moving object classification, trajectory analysis, and reclassification of unrecognised objects by processing the LiDAR data, pre-processed in a .osef format, thereby transforming it to make it suitable for simulation. The proposed solution for the monitoring of urban traffic is demonstrated by the usage of SUMO (Simulation of Urban MObility) for performing simulations and a Random Forest model for unrecognized object reclassification to pre-existing vehicles and pedestrian classes. The architecture of the proposed workflow can possibly be applied in other similar urban settings, providing a scalable solution for both traffic management and urban planning. The study’s results support the wider use of urban digital twin principles in ITS by highlighting the value of advanced modelling tools and high-quality data in addressing today’s urban transportation challenges.

https://doi.org/10.5194/isprs-annals-X-4-2024-411-2024