A Congestion Detection Framework based on Vehicle-Counter CNN and Self-Learning Critical Density Approach

Abstract

Density is a crucial indicator for the level of services on highways. Due to the limitations of monitoring devices, speed, occupancy, and volume to capacity are used as surrogates to help authorities determine road congestion. Attempts have been made to utilize computer vision-based methods to retrieve accurate road density estimation. However, two significant problems emerge for CV-based methods: 1) Features of vehicles are not clear enough to be recognized in low-resolution or dense scenes. 2) The lack of knowledge of road length hinders the density calculation. This paper intends to address these limitations by developing a congestion detection framework, including the Vehicle-Counter Convolutional Neural Network (VC-CNN) and the Self-Learning Critical Density Component (SL-CDC). The VC-CNN produces estimations of vehicle count by creating accurate density maps. The SL-CDC divides the road section into two homogeneous cells and distinguishes the dynamic characteristics of traffic flow, including shockwaves, to derive the critical density of the road section and identify forming congestion. This framework has been validated with a wide range of experiments and applied to field tests on the G2 Expressway in China with considerable accuracy and reliability, satisfying the demand of the transportation authorities to monitor and identify the forming congestion.