Pixels to Signals: A Real-Time Framework for Traffic Demand Estimation
This addresses traffic management for urban planners, but it is incremental as it focuses only on the detection component of a larger framework.
The paper tackles traffic congestion by developing a vehicle detection system that analyzes camera feeds to compute background roadways and uses DBSCAN clustering to identify vehicles, achieving computational efficiency for real-time deployment.
Traffic congestion is becoming a challenge in the rapidly growing urban cities, resulting in increasing delays and inefficiencies within urban transportation systems. To address this issue a comprehensive methodology is designed to optimize traffic flow and minimize delays. The framework is structured with three primary components: (a) vehicle detection, (b) traffic prediction, and (c) traffic signal optimization. This paper presents the first component, vehicle detection. The methodology involves analyzing multiple sequential frames from a camera feed to compute the background, i.e. the underlying roadway, by averaging pixel values over time. The computed background is then utilized to extract the foreground, where the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm is applied to detect vehicles. With its computational efficiency and minimal infrastructure modification requirements, the proposed methodology offers a practical and scalable solution for real-world deployment.