A Novel Context-Based Approach of Identifying Congestion Detection

Abstract

Traffic congestion detection is one of the major key issues in traffic management. The existing works, in general, focus on the speed and density of the vehicles for detecting congestion. But the contextual information could be another major input that affects the performance of congestion detection algorithm. Practically, a context can be used to characterize the situation of an entity. Thus the solutions, those are not considering contexts, may not be suitable for the real-life application. In this work, an attempt has been made to offer a context-based probabilistic graph model. The model is capable to generate a new context and delivers the result accordingly. The simulation of the proposed mechanism has been done and the results substantiate the claim i.e. the effectiveness of the proposed model.