Data-Driven Optimization of Urban Traffic using AI and Real-Time Analysis

Abstract

Optimizing urban traffic is a significant problem for cities all over the world. This research aimsto tackle this issue by utilizing real-time analysis and artificial intelligence (AI). The project's keycomponents are data collection, the creation of a 2D map, object detection using the YOLO algorithm, 3Dsegmentation, visualization, and data integration. To ensure the precision of data collection, we employ amulti-GNSS RTK approach for precise location determination. This method allows us to generate exactcoordinates for urban road networks, which provides the basis for additional research. We are able todisplay urban traffic flows on a 2D visualization map, allowing us to spot crowded locations and improvetraffic flow. The YOLO method is used in conjunction with 3D segmentation to identify objects. Throughtraining, we allow this algorithm to recognize and categorize a wide range of objects, including movingvehicles, pedestrians, and particular vehicle types (such as minibusses and taxis), which significantlycontribute to traffic congestion. Our project makes better use of real-time object detection to enable wellinformed decision-making and improve understanding of the traffic situation.