Traffic congestion prediction based on Hidden Markov Models and contrast measure

Traffic congestion is an important socio-economic problem that swelled in the last few decades. It affects the social mobility of people, length of trips, quality of life, and the economy of countries. As a major problem in most countries, it has been tackled by governments, universities, and advanced research using intelligent transportation systems (ITS) to solve the problem or at least ease its adverse effects. Hidden Markov Models (HMM) represent one of the methods that are suitable for congestion prediction. In this paper, a new model, based on Hidden Markov Model and Contrast, is proposed to define the traffic states during peak hours in two dimensional space (2D). The proposed model uses mean speed and contrast to capture the variability in traffic patterns. Empirical evaluation shows that the proposed approach has improved prediction error in comparison to HMM related work and neuro-fuzzy approaches.