Smart Predictor: A Road Incident Prediction System using Hybrid Data Mining Approach on Traffic Data

Tackling urban road congestion by means of ITS technologies, involves a number of key challenges. One such challenge is related to the accurate detection of traffic incidents in urban networks for more efficient traffic management. It combines K-means Clustering algorithm and Naive bayes techniques in light of the fault diagnosis theory. In the core of the proposed approach lies a more efficient feature extraction technique, based on the dynamic characteristics of data corresponding to those vehicles that are involved in incidents. In the core of the proposed approach lies a more efficient feature extraction technique, based on the dynamic characteristics of data corresponding to those vehicles that are involved in incidents. Our work observe show dynamic aspects of measured data can be exploited for extracting features that result in measurable improvement of the incident detection rate by the application of a Naive bayes.