Urban Area Congestion Detection and Propagation Using Histogram Model

Abstract

Detecting congestion in urban areas is critical and creates a myriad of complications. Intelligent Transportation Systems (ITS), which are trending in recent years, are used by researchers to engage problems related to congestion and transportation. However, due to the open access in urban area structures, it is less feasible to handle rife data that is generated from vehicles and infrastructure. On the grounds, ITS demands a reliable methodology that uses the data's effectively to detect the congestion. In this paper, we present a novel congestion estimation model for urban areas that leads to predict the congestion propagation. It uses a histogram-based model on a window time basis to make the data transfer substantially minimum and keep the system robust. Due to its simplicity, it can be practically implemented in real time for any nature of roadways. Simulation results, with different scenarios, show that the proposed model is detecting the congestion estimation effectively and leads to predict the congestion propagation for the near future.