Pixels to Signals: A Real-Time Framework for Traffic Demand Estimation

• URL: http://arxiv.org/abs/2510.24902v1
• Date: Tue, 28 Oct 2025 19:04:53 GMT
• Title: Pixels to Signals: A Real-Time Framework for Traffic Demand Estimation
• Authors: H Mhatre, M Vyas, A Mittal,
• Abstract summary: A comprehensive methodology is designed to optimize traffic flow and minimize delays.<n>The framework is structured with three primary components: (a) vehicle detection, (b) traffic prediction, and (c) traffic signal optimization.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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