Enhanced video-based traffic management application with virtual multi-loop crate

The growth in urban population leads to congestion of vehicles in city roads. The quality of transportation is improved by latest technologies of Intelligent Transportation Systems (ITS) applications. Any ITS application relies heavily on sensors for their data collection for an efficient management, control and planning transportation. In this paper, the video-based traffic data collection and their techniques are improved using the proposed Virtual Multi-Loop Crate (VMLC) framework. VMLC uses the spatial colour information for image processing without losing information. The outcomes of the proposed framework are used to estimate traffic statistics and parameters that are essential for ITS applications. In this paper, the outcomes of VMLC are analysed for its accuracy and efficiency using Congestion Level (CoLe) estimation application. It is seen from the results that VMLC framework improves the quality of data collection for any video-based ITS applications.