Intersection Queue Length Estimation Using Connected Vehicles
Abstract
Traffic congestion and safety challenges in urban areas are key issues for today’s societies, creating a need for more efficient traffic control systems. This paper introduces a methodology for estimating traffic states at intersections utilizing data derived from Connected Vehicles (CV). The proposed methodology is based on the estimation of intersection queue lengths with unknown CV penetration rate. The results indicate that even with a low CV penetration rate within the traffic flow, it is feasible to accurately estimate queue lengths despite an unknown penetration rate.
Fields of Study & Subjects
Traffic control and management
Traffic Prediction and Management Techniques
Transportation Systems and Logistics
Publication details
- Journal / source
- Transportation research procedia
- Volume
- 93
- Pages
- 903–908
- Publication date
- 1 January 2026
- Language
- EN