A prediction model for traffic emission based on interval-valued intuitionistic fuzzy sets and case-based reasoning theory

Prediction of traffic emission is very important to management actions for traffic emission reduction. To overcome the shortcomings of ordinary prediction methods in previous studies, a novel model for the prediction of traffic emission is proposed for the combination of interval-valued intuitionistic fuzzy sets and case-based reasoning theory. Based on analysis of the factors that affect the traffic emission, a characteristic factor matrix of the source cases was constructed. Then, an interval-valued intuitionistic fuzzy set was introduced in order to describe the uncertainty of the case, and the source case that was the most similar to the target case, was picked out by calculating the similarities between the source cases and the target case, therefore leading to the improvement in prediction accuracy. Finally, a case study was conducted to evaluate the effectiveness of the constructed prediction model.