Graph-Based Density Estimation for Real-Time Traffic Congestion Monitoring

Traffic density estimation plays a crucial role in traffic management. This paper introduces a novel approach for accurately estimating traffic density using a graph-based density estimation algorithm applied to video data. Our proposed method leverages an advanced object detection model capable of real-time vehicle detection across various sizes and types. By processing video frames, the system detects and counts vehicles, which are then used to construct a graph representation of the traffic. Combining the strengths of vehicle detection algorithms with graph-based density estimation techniques, our approach effectively estimates traffic density in real-world scenarios. Experimental evaluations conducted on real-world video data validate the effectiveness of our integrated method. The results demonstrate that our approach accurately identifies congested areas, providing valuable insights into traffic flow dynamics. This facilitates timely interventions, such as signal optimization or rerouting strategies, ultimately alleviating congestion and enhancing overall traffic efficiency.